Schizophrenia can lead to significant and long-lasting deficits in patients' functionality. The present study aims to identify a theoretical index that predicts the ability of an antipsychotic to improve the functionality of patients with schizophrenia. An advantage of such a theoretical index is that it represents an attempt to supplement with theoretical information the data coming from meta-analysis and systematic reviews and could directly compare antipsychotics. Aspects such as pharmacodynamics, pharmacokinetics, and pharmaceutical forms have been considered. A total of 29 typical and atypical antipsychotics were analyzed. It was constructed the Predictive Impact of Antipsychotics on Functionality Index (PIAF). This parameter considers pharmacodynamics and administration comfort, each expressed through other parameters. PIAF is higher for antipsychotics with both high Pharmacodynamic Scores, which are correlated only with the active substance, and good Comfort of Administration Scores, which are correlated with both the pharmaceutical forms of antipsychotics and the active substance. The best ranking was obtained by the only partial agonist dopaminergic D2 antipsychotic with LAI formulation (aripiprazole LAI), followed by the atypical LAI antipsychotic with the rarest administrations (paliperidone LAI 6M). Given this ranking, a better antipsychotic would result from the combination of a partial dopaminergic agonist with the lowest possible administration frequency. According to PIAF, the most favourable for the functionality are atypical LAI antipsychotics. The partial agonist mechanism of D2 dopamine represents an advantage. Based on this functionality index, the clinical psychiatrist could select the most suitable antipsychotic for patients, with the ultimate goal of achieving individual maximum potential.

Industrial products e-commerce refers to the specific application of the e-commerce concept in the field of industrial products transactions. It enables industrial enterprises to conduct transactions via Internet platforms and reduce circulation and operating costs. Industry literature such as policies, reports and standards related to industrial products e-commerce contains abundant and crucial information. Through systematical analysis of the literature information, it is beneficial to explore and grasp the development characteristics and trends of the industrial products e-commerce. To study the characteristics and development status of industrial products e-commerce in China, literature comprising 18 policy documents, 10 industrial reports and 5 standards is deeply analyzed by employing text mining methods. Firstly, natural language processing (NLP) technology is utilized to pre-process the text data related to industrial products commerce. Then, word frequency statistics and TF-IDF keyword extraction are performed, and the results of word frequency statistics are visually displayed. Subsequently, the feature set is obtained by combining the manual screening method. The original text corpus is used as the training set by employing the skip-gram model in word2vec, and the feature words are transformed into word vectors in the multi-dimensional space. The k-means algorithm is used to cluster the feature words into groups. Moreover, the latent dirichlet allocation (LDA) method is also utilized to further group and discover the features. The research results based on text mining provide supportive decisions for uncovering the development characteristics and trends of industrial products e-commerce in China.

The current developments in the theory of quantum static triplet correlations and their associated structures (real r-space and Fourier k-space) in monatomic fluids are reviewed. The main framework utilized is Feynman’s path integral formalism (PI) and the issues addressed cover quantum diffraction effects and zero-spin bosonic exchange. The structures are associated with the external weak fields that reveal their nature, and due attention is paid to the underlying pair level structures. Without the pair level one cannot fully grasp the triplet extensions in the hierarchical ladder of structures, as both the pair and the triplet structures are essential ingredients in the triplet response functions. Three general classes of PI structures do arise: centroid, total continuous linear response, and instantaneous. Use of functional differentiation techniques is widely made and, as a bonus, this leads to identify an exact extension of the “classical isomorphism” when the centroid structures are considered. In this connection, the direct correlation functions, as borrowed from classical statistical mechanics, play a key role (either exact or approximate) in the corresponding quantum applications. Additionally, as an auxiliary framework, the traditional closure schemes for triplets are also discussed, owing to their potential usefulness for rationalizing PI triplet results. To illustrate some basic concepts, new numerical calculations (path integral Monte Carlo PIMC and closures) are reported. They are focused on the purely diffraction regime and deal with supercritical helium-3 and the quantum hard-sphere fluid.

Bee Pollen (BP) has many advantageous properties relying on its multitargeting potential, a new tendency in handling many challenging illnesses. In cancer and neurodegeneration, the antioxidant, anti-inflammatory, immunomodulatory, and antimutagenic effects of BP could be of unequaled importance and need further pharmacological and clinical investigation. Although being still limited, available data interestingly spotlights some floral sources with new and promising activities in-line with this investigation. In addition, we have identified many crucial bioactivities that are widely recognized to individual BP compounds but remain completely untapped in this valuable bee cocktail. A wide range of these compounds have been recently found to be endowed with great potential in modulating pivotal process in neurodegeneration and cancer pathophysiology, such as epigenetic regulation, microbiota composition and function, autophagy, cell death mechanisms, protein misfolding and aggregation and many other effects that we will detail in our current work. In addition, some widely present compounds in BP have only recently been isolated, while the number of studied BPs remain extremely limited compared to the endless pool of plant species that are distributed worldwide. Nevertheless, we have concomitantly elucidated that clinical profits from these promising perspectives are still impeded by some challenging hurdles such as limited bioavailability of the studied phytocompounds, diversity and lack of phytochemical standardization of BP, and the difficulty of selective targeting in some pathophysiological mechanisms. We finally present some interesting insights to guide future research and pave the way for urgent and simplified clinical investigations.

Introduction: The care of dependent people is eminently family-oriented, and often, there is a high level of dedication to this family care. Constant and continuous care leads to a series of negative psychological consequences. Social support has been related to improved mental health in family caregivers. We found heterogeneous results regarding the relationship between the types of social support received and the perceived level of support. In addition, to our knowledge, no reviews analyze this relationship among family caregivers. Therefore, we aimed to systematically syn-thesize the relationships between perceived and received social support in informal caregivers. Methodology: We have carried out a quantitative systematic review with meta-analysis, regis-tered in PROSPERO (id: CRD42023470047); the systematic search was carried out in the following databases: PubMed, CINAHL, PsycINFO, and Scopus, until November 2023. After the selection and review of the results, 12 studies were obtained, two of which were eliminated due to a high risk of classification bias. Regarding the results, a medium size positive statistical association was found (r = 0.43). The results were consistent, accurate, and robust. The Trim and Fill test showed a vari-ation of 7%. Subgroup analysis indicated no differences in the age group of the people cared for (adults or children), selection bias, and confounding bias. In conclusion, perceived social support is related to more social support received by family caregivers.

Dedifferentiated liposarcoma (DDLPS) is a rare and aggressive type of cancer primarily reported in humans, with no documented cases in animals. In this study, we present the first case of DDLPS in a wild amphibian species, Maculopaa medogensis. The tumor was discovered during a routine examination and diagnosed through a combination of advanced diagnostic methods, including micro-CT imaging, gross anatomical inspection, histological analysis, and immunohistochemistry. The tumor exhibited both well-differentiated and dedifferentiated components, characteristic of DDLPS, with evidence of tissue invasion and multiple metastases. Immunohistochemical analysis revealed strong positive expression of markers such as S100A4, CDK4, MDM2, and CD34, while Leptin expression was negative, further confirming the diagnosis. This is the first reported case of DDLPS in a non-human species, expanding our understanding of cancer in wildlife and underscoring the significance of amphibians as environmental indicators. These findings provide valuable insights for veterinary medicine and wildlife conservation, particularly regarding the role of environmental stressors in cancer development. This study highlights the need for comprehensive diagnostic approaches in wildlife pathology.

Objective: To evaluate the effect of different surface treatment methods on the shear bond strength (SBS) of metal brackets bonded to two types of CAD/CAM composite restorations: milled and 3D-printed. Materials and Methods: A total of 160 flat-shaped specimens (10×10×2 mm³) were prepared from four different CAD-CAM composites; two milled (Lava Ultimate™ [LU], and Grandio™ [GR]) and two 3D-printed (Crowntec™ [CT] and C&B Permanent™ [CB]). These specimens underwent thermocycling (5000 cycles at 5-55 °C), then were categorized based on the surface treatment into four groups (n=10): Group C (control, no surface treatment), Group HF (treated with 9.6 % hydrofluoric acid), Group DB (mechanical roughening by a diamond bur), and Group SB (sandblasting using aluminum oxide). Metal brackets were bonded, stored in artificial saliva for 24 hours, then thermocycled again. Shear bond strength (SBS) was tested using a universal testing machine until bracket debonding occurred. The adhesive remnant index (ARI) was assessed using a stereomicroscope to quantify the residual adhesive following debonding. Result: Regarding material, GR and LU restorations had significantly higher SBS values compared to CT and CB, ranging from 13.90 MPa to 20.35 MPa. Regarding surface treatment, SB and HF groups showed significantly higher SBS values. The ARI scores showed different adhesive modes of failure, with higher instances of scores 0 and 1 which indicate no or minimal adhesive remaining. Conclusion: Both milled and 3D-printed materials had adequate SBS for clinical use, with milled materials showing superior results. Surface treatments like sandblasting and HF significantly improved bond strength, with adhesive failure being common.

Machine unlearning is a critical process designed to allow machine learning models to forget or remove specific data upon request, particularly for privacy protection. While the primary objective of unlearning is to safeguard sensitive information, it introduces substantial privacy and security risks. This paper explores the two major categories of threats to machine unlearning: privacy attacks and security attacks. Privacy attacks, such as membership inference and model inversion, exploit residual information to infer or reconstruct sensitive data that should have been erased. Security attacks, particularly specific data poisoning, involve the injection of malicious data into the training process, which may leave lingering effects even after the unlearning process. In this paper, we provide an in-depth examination of these threats and propose several defense mechanisms. Differential privacy, adversarial training, and query limitations are highlighted as key defenses against privacy attacks, while data validation, adversarial examples, and post-unlearning audits are critical in mitigating security risks. Additionally, we discuss emerging methodologies like data provenance tracking and fine-tuning as crucial for ensuring unlearning processes are thorough and effective. Through these analyses, we aim to provide a comprehensive framework for strengthening the security and privacy of machine unlearning systems, enabling their broader adoption across industries such as healthcare, finance, and AI-driven technologies.

Omega-3 polyunsaturated fatty acids (ω-3 PUFAs, n-3 PUFAs), including eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), and alpha-linolenic acid (ALA), are essential polyunsatu-rated fats primarily obtained from fatty fish and plant-based sources. Compelling evidence from preclinical and epidemiological studies consistently suggests beneficial effects of ω-3 PUFAs on bone health and healthy aging processes. However, clinical trials have yielded mixed results, with some failing to replicate these benefits seen in preclinical models. This contraindication is mainly due to challenges such as low bioavailability, potential adverse effects with higher doses, and susceptibility to oxidation of ω-3 fatty acids, hindering their clinical effectiveness. This review provides an in-depth analysis of recent findings, focusing on clinical perspectives and encom-passing all relevant published studies to date, including preclinical and epidemiological research, with an emphasis on the role of omega-3 fatty acids in promoting bone health and supporting healthy aging. Additionally, it explores strategies to improve ω-3 PUFA efficacy, including na-noparticle encapsulation and incorporation of specialized pro-resolving mediators (SPM) derived from DHA and EPA, to mitigate oxidation and enhance solubility, thereby improving therapeutic potential. By consolidating evidence from various studies, this review underscores current in-sights and future directions in leveraging ω-3 PUFAs for therapeutic applications.

This study examines the evolution of the tourism industry in Chile during the period 2013-2019, focusing on its contribution to the country's Gross Domestic Product (GDP). Using data from the National Tourism Service (SERNATUR) and the Undersecretariat of Tourism, it analyzes the approximation of tourism GDP and its share in the Chilean economy. The results indicate a sustained growth of the tourism sector, with a contribution to GDP that reached 3.3% in 2019. The main Tourism Characteristic Activities (TCAs) contributing to this growth are identified, highlighting administrative and support activities, food services and accommodation. The study reveals growth patterns, fluctuations in the contribution of different TCAs, and the impact of external factors on the sector's performance. This research provides valuable insights for the formulation of tourism development policies and strategies in Chile.

Satellite navigation spoofing is a major challenge in the field of satellite/inertial integrated navigation security. To effectively enhance the anti-spoofing capability of low-cost GNSS/MEMS-SINS integrated navigation system, this paper proposes a method integrating a dual-antenna global navigation satellite system (GNSS) and a micro-inertial measurement unit (MIMU) to determine the two-dimensional (2D) directions of spoofing signal sources. The proposed method evaluates whether the single-difference carrier-phase measurements conform to the corresponding directions given in ephemeris files, and employs the micro-inertial navigation technology to determine the 2D directions of the signal source. Based on a set of short-baseline dual-station measurements, the accuracy of the proposed method in determining the 2D azimuths of satellites in synchronous orbits is verified, and the deviation from the real value is evaluated. The experimental results show that the proposed method can effectively identify the spoofed satellite signals while providing high-precision direction information at three different distances: 100m, 10km, and 36000km.The two-dimensional angles errors do not exceed 0.2 rad, 0.05 rad, and 0.01 rad, respectively.

This study reports the use of Nanotrap® Microbiome A Particles (NMAPs) to capture and concentrate viruses from diluted samples/specimens to improve their recovery and sensitivity of detection by real-time PCR/RT-PCR (qPCR/RT-qPCR). Five highly infectious animal disease viruses including goat pox virus (GTPV), sheep pox virus (SPPV), lumpy skin disease virus (LSDV), peste des petits ruminants virus (PPRV), and African swine fever virus (ASFV) were used in this study. After capture, the viruses remained viable and recoverable by virus isolation (VI) using susceptible cell lines. To assess efficacy of recovery, the viruses were serially diluted in phosphate buffered saline (PBS) or Eagle’s Minimum Essential Medium (EMEM) and then subjected to virus capture using NMAPs. The NMAPs and the captured viruses were clarified on a magnetic stand, reconstituted in PBS or EMEM, and analyzed separately by VI and virus-specific qPCR/RT-qPCR. The results of the qPCR/RT-qPCR showed up to 100-fold increase in the sensitivity of detection of the viruses following virus capture compared to the untreated viruses from the same dilutions. NMAPs were used to capture and concentrate viruses from clinical specimens to determine diagnostic sensitivity (DSe) that were comparable (100%) to the untreated specimens. NMAPs were also used to capture spiked viruses from EDTA whole blood (EWB). Virus capture from EWB was partially blocked, most likely by hemoglobin (HMB), which also binds NMAPs and outcompetes the viruses. The effect of HMB could be removed by either dilution (in PBS) or using HemogloBind™ (Biotech Support Group; Monmouth Junction, NJ) that specifically binds and precipitates HMB. Enhanced recovery and detection of viruses using NMAPs can be applicable to other highly pathogenic animal viruses of agricultural importance.

High-intensity interval training (HIIT) is widely used in team sports, particularly in soccer. This study aimed to investigate the effects of a short-term, progressive- load HIIT program on the performance of under-15 soccer players. The sample consisted of 30 adolescent soccer players, aged 14, who were randomly divided into two groups: the control group (CG, n=15) and the intervention group (EX, n=15), which performed additional HIIT sessions. The duration of the training program was six weeks, with progressive overload applied every two weeks. Performance indicators, including speed over 10 and 30 meters, change of direction (COD) to the right and left (505 right and left), maximal oxygen uptake (30-15IFT), and repeated sprint ability (RSA) (RSAtotal, RSAmean, RSAdecrement), were measured before and after the training program. Significant improvement were observed in the intervention group in COD (505 right and left), 30-15IFT, and RSAtotal and RSAmean (p=0.027, p=0.005, p=0, p=0.015, p=0.031, p=0.028, respectively). The findings of the present study suggest that a short-term, progressively overloaded HIIT program can enhance direction change ability, aerobic capacity, and repeated sprint performance.

Brain health is a matter of great importance as we age, where memory loss and other cognitive declines become slowly apparent. There are numerous natural solutions, called nootropics, that can help mitigate this natural process. While some, such as ginkgo biloba, have been traditionally used for centuries, in many cases there is a lack of scientific evidence on their potential mechanisms and efficacy. To this end, the objective of the current study is to evaluate and compare several standardized herb extracts with potential nootropic effects, and from this analysis develop a formula and confirm its effect in healthy volunteers with mild cognitive impairment. A total of 9 standardized herb extracts were chosen based on previous scientific evidence and tested in human neural stem cell cultures exposed to oxidative stress. The expression of nerve growth factor (NGF), NRF2 and vascular endothelial growth factor (VEGF) were also analyzed. As a result, it was observed that an extract from Salvia officinalis revealed to have the most noticeable effect among the herbs analyzed. In further tests it was confirmed that its combination with rutin, a phenolic compound with potential nootropic effects, increased brain derived neurotrophic factor (BDNF) expression, as well as inhibiting acetylcholine esterase (AChE) activity. Finally, a randomized, double-blind placebo-controlled clinical study was conducted, testing the effects of the combination at two different doses, for 12 weeks, in healthy volunteers aged 50+ with mild cognitive impairment. Two questionnaires were applied, the MoCA and ADAS-Cog. As a result, both doses significantly improved several characteristics related to memory, particularly memory recall, with a more noticeable effect in the higher dose. Therefore, the results suggest that the long-term intake, at least 6-12 weeks, of a natural herb blend comprised of Salvia officinalis and rutin can help improve certain brain health-related parameters in healthy subjects with mild cognitive impairment.

Optimal upper extremity motor control and range of motion are necessary to achieve even the basic Activities of Daily Living (ADL) function. Stroke, with resulting hemiparesis, can significantly and negatively impact an individual’s ADL function. Functional Independence Measure (FIM) self-care score can provide assessment of what aspects and to what degree ADL functions are impaired. FIM self-care assessment can also track changes in ADL function during stroke recovery and rehabilitation. Recently, sensor-acquired 3D motion analysis of stroke patients’ upper extremity has shown promise as a potential alternative to assess ADL function. This observational study evaluates whether the sensor-acquired upper extremity reachable workspace (RWS) measure correlates with clinician-evaluated FIM self-care score in stroke patients. Seventeen patients with stroke were enrolled in the study. FIM self-care, NeuroQoL upper extremity, and reachable workspace outcome measures (relative surface area, RSA) were collected upon rehabilitation hospital admission, at discharge, and at 3-month visit. Pearson and Spearman’s rank correlation coefficients as well as multiple linear regression analyses were used to determine the relationships between FIM self-care, NeuroQoL, and reachable workspace RSAs. Moderately strong correlation between total reachable workspace RSA and total FIM self-care score at discharge and at 3-month were noted (r=0.619, r=0.661, p<0.05), and similarly strong correlation was also noted with the upper extremity NeuroQoL total score (r=0.690, r=0.815, p<0.05). Multiple linear regression analyses revealed a change in bilateral total RSA of 0.1 unit from admission to 3-month follow up correlated with respective change in FIM self-care score of 2.011 points (95%CI: 0.663-3.360). Longitudinal improvement in ADL function during stroke rehabilitation and recovery process is correlated with improvement in reachable workspace.

Modern methods of designing and constructing hydraulic pumps can no longer be imagined without the use of appropriate mathematical models, phenomena and processes that take place in concrete constructions. A mathematical model of a process represents an analytical interpretation with certain assumptions. Obtaining a mathematical model requires detailed theoretical research, based on knowledge of the laws of fundamental sciences and scientific disciplines in order to fully understand and interpret the process, based on which assumptions are adopted and model equations are defined. As mobile machines continue to evolve, so do the demands on their hydraulic systems. Efficiency, automation and other major industry trends present ongoing development opportunities for hydraulic systems used in mobile applications. Hydraulic systems remain an important component in a range of mobile applications, from construction and agricultural equipment to heavy trucks because of the power density they can provide for a range of work functions. The power density of hydraulics remains unmatched in many applications. The pump, as an essential hydraulic component, converts mechanical energy into hydraulic energy with a relatively small amount and speed of fluid.

Per- and polyfluoroalkyl substances (PFAS) are arguably the most common water contaminants in the world today. While several research experiments have been done to understand and remove PFAS from the environment, there is still a lot of unknown. Little has been known about the use of Machine learning (ML) to understand PFAS. This work hence reviews some leading ML approaches and applications in PFAS studies in the distribution, transport, removal, and occurrence predictions of PFAS. Several evaluation matrices were examined and used to perform this function. There are still a lot of areas whereby ML can be used to improve our PFAS knowledge base, some of these were briefly stated in this review.

It is argued that the compressibility of individual atoms is distinct from the compressibility of matter as a whole. The latter arises from variations in interatomic spacing, whereas the former is a purely quantum effect, characteristic of a given atomic species. Its magnitude can be deduced by solving the Schrödinger equation for an individual many-electron atom with modified external boundary conditions. Over a wide range of parameter space, quantum dilation or compression remains isoelectronic for neutral atoms but, at ultra-high pressures, reordering of their shell structure can occur. Their compression then ceases to be isoelectronic and their chemical properties, as well as their positioning in the Periodic Table, undergo sudden change.

The aim of this work is to propose an angular positioning solution for the round cutting insert of an indexable insert torus milling cutter, dedicated to the multi-axis milling process under HSM conditions. The solution was based on the development of a wear model using a nonlinear estimation method with an eigenfunction, the development of a tool life model taking into account parameter of the cutter blade working angle and the method of reference marking and insert mounting, as well as the measurement methodology. The own function adopted to approximate the wear of the tool over its useful life is adequate and provides a prediction accuracy of 90%. The developed procedure for angular positioning and measurement enables the entire cutting edge of the cutting blade to be fully used, so that the total lifespan of the tool as an indexable insert tool is significantly extended. In addition, the measured largest defect values between the worn cutting edge and the nominal outline of the insert indicate the location of “notching” type wear.

Glioblastoma is a challenge in neuro-oncology, with survival significantly influenced mainly by extent of resection and molecular markers. Despite advancements, the prognosis for IDH-wildtype glioblastoma remains poor, particularly when surgical resection is not possible. However, some patients exhibit unexpectedly extended survival despite the extent of resection. This study aims to analyze the determinants that contribute to these atypical survival rates among glioblastoma patients who have had solely biopsy procedures. We conducted a retrospective analysis of patients diagnosed with IDH- wildtype glioblastomas at our institution from 2017 to 2021, who underwent biopsy only. The study focused on evaluating the impact of demographic characteristics, clinical features, molecular markers and treatment modalities on survival outcomes (Overall Survival (OS) and Progression-Free Survival (PFS)). Statistical analyses included survival analysis and logistic regression for evaluating associations between OS and preoperative characteristics and postoperative treatments. The cohort included 99 patients, with a median age at diagnosis of 65.5 years. Median OS and PFS were 6.0 and 3.6 months, respectively. The multivariate analysis revealed that higher KPS scores before biopsy, no contrast uptake on imaging and any adjuvant therapy, particularly the use of bevacizumab, were independently associated to increased OS (HR=0.97, p=0.009. HR=0.7, p=0.015; HR=0.27, p=0.002, respectively). Out of 99 patients, 77.8% survived past the 3-month threshold, with 87.0% of this receiving adjuvant treatment. Only 8% of patients survived past 24 months, and in this group of patients, MGMT Methylation was observed in just 25% of cases. Kaplan-Meier analysis indicated a better prognosis with any type of adjuvant therapy across all patients, particularly so in those with KPS ≥70. Age did not significantly affect survival outcomes (OR=1.00, p=0.835). Our findings reveal that any adjuvant treatment, no contrast uptake on imaging and higher preoperative KPS are key determinants of survival in IDH wildtype glioblastoma and should therefore be considered when deciding whether to perform a biopsy.

A prominent class of reactions, reductive aminations are used extensively in research labs and industry to synthesize amines; which are applied in a variety of medicines, agrochemicals, and biomolecules. Amine compounds are essential in many aspects of life; amino groups exist in many natural and agricultural products. New 4-(benzylamino)benzoic acid derivatives with general formula of C14H12NO2R, where [R= (H), (4-Cl), (4-NMe), (4-Br), (3-NO2), (4-NO2), (2-OMe), (3-OMe), (4-OMe), (2,3-OMe), (3,4-OMe), (2-OH, 3-OMe), (3-OMe, 4-OH), (3,5-OMe, 4-OH), (2-OH, 5-Br), (3-OH), (4-SMe), (2,3-OH), (3-CF3)] (compounds 1-19) were obtained by reductive amination reaction via reacting p-amino benzoic acid with benzaldehyde derivatives and sodium borohydride. All prepared compounds has been characterized by a variety of spectroscopic techniques (HRMS, FT-IR, 1H, and 13C{1H} NMR) and elemental analysis. Furthermore, the structure of some of the synthesized derivatives were confirmed by single crystal X-ray diffraction (R = 4-Cl, 4-Br, 2-OMe, 3-OMe, 4-OMe, 2,3-OMe), compounds 2, 4, 7-9 and 11, respectively. Moreover, some of these new compounds have shown somehow moderate antibacterial activities against different strains of bacteria (gram-positive and gram-negative) with MIC values ranging from 64-256 µg/ mL. The anticancer activities of all compounds (IC50 values, µM) were evaluated against different cell lines, HGF (Fibroblasts; normal cell line), A549 (Non-Small Cell Lung Cancer cell line) and H69 (Small Cell Lung Cancer cell line). Compound 18 showed a considerable IC50 value of 90.69 against A549 and an excellent value of 32.22 and H69, respectively.

Five new species of the spider genus Toxopsoides Forster & Wilton, 1973 are described from Tasmanian surveys by DPIPWE and Bush Blitz (T. driesseni, sp. nov, T. tarkayna, sp. nov., T. wybalena, sp. nov., T. deux, sp. nov.) and one from an intensive IBISCA survey (T. ibisca, sp. nov.) in southern Queensland. It is suggested that Toxopsoides is transferred to Cycloctenidae with which it shares most characters.

In today’s business landscape, customer retention has become a critical factor for sustainable growth. Many companies continue to focus on acquisition, which leads to high costs and constant customer turnover. This article highlights the importance of retention, proposing a shift in focus toward loyalty as the key to maximizing customer lifetime value (LTV). Improving retention not only increases LTV but also reduces acquisition costs, enhances virality, and provides financial stability. This paper introduces a scientific approach that allows companies to identify the underlying causes of low retention through observation, hypothesis formulation, and controlled experimentation. Using tools such as data analysis and A/B testing, companies can iteratively optimize their retention strategy, adjusting interventions in real-time based on the results obtained. Continuous experimentation emerges as an indispensable tool for navigating market complexity, enabling organizations to learn and adapt quickly. This data-driven approach not only improves retention but also drives long-term growth by building strong relationships with customers. In an increasingly competitive environment, companies that prioritize retention and experimentation are better positioned to achieve resilient and profitable growth.

Several medical graduates, avoid surgical specialties especially those with complex and delicate anatomy due to their doubt in their visual spatial abilities. Many patients are deemed inoperable, particularly in the field of pediatric cardiology, due to the complexity of the anatomy. On a similar note, stratification of risk of cardiac interventions, and decision making, is a room of significant person-based and center-based bias. Two inter-related technologies, namely immersive virtual reality (VR) and artificial intelligence have made impossible things possible and can help to standardize decision making strategies. This review aimed at reviewing the main anatomical and lesion-based scopes of these advancements in the field of cardiac care, with a special focus on pediatric cardiology.

A system of two nonlinear difference equations under stochastic perturbations is considered. Nonlinearity of the exponential type in each equation of the system under consideration depends on all variables of the system. The stability in probability of a positive equilibrium of the system is studied via the general method of Lyapunov functionals construction and the method of linear matrix inequalities (LMIs). The obtained results are illustrated via examples and figures with numerical simulations of solutions of a considered system of stochastic difference equations. The proposed way of investigation can be applied to nonlinear systems of higher dimension and with other types of nonlinearity, for both difference equations and for delay differential equations.

As an important civil and military infrastructure, airport runway pavement is faced up with threats from cluster munitions, since it is vulnerable to projectile impact with internal explosion. Aiming at the damage assessment of island airport runway pavement under impact, this work dealt with discrete modeling of rigid projectile penetration into concrete pavement and calcareous sand subgrade multi-layer structure. First, Discrete Element Method (DEM) is introduced to model concrete and calcareous sand granular material features like cohesive fracture and strain hardening due to compression, with mesoscale constitutive laws governing the normal and shear interactions between adjacent particles. Second, sequent DEM simulations of uniaxial and triaxial compression were performed to calibrate the DEM parameters for pavement concrete as well as subgrade calcareous sand. Piror to multi-layer structure investigations, penetration into sole concrete or calcareous sand is validated in terms of projectile deceleration and depth of penetration (DOP) with relative error

Although large language models demonstrate impressive capabilities, they sometimes generate irrelevant or nonsensical text, or produce outputs that deviate from the provided source input—an occurrence commonly referred to as hallucination. To mitigate this issue, we introduce a novel decoding method that incorporates both factual and hallucination prompts(DFHP), utilizing a contrastive output distribution to highlight the disparity in output probabilities between model predictions influenced by factual prompts and those affected by hallucination prompts. Experiments on both multiple-choice and text generation tasks show that our approach significantly enhances the factual accuracy of large language models without requiring additional training.

Aim: This study aims to analyze the structure of the co-authorship network in viral hepatitis research from 2000 to 2023 using data from the Web of Science (WoS) Core Collection. The objective is to identify key researchers, evaluate the collaborative dynamics, and understand the evolution of scientific contributions in this field. Method: This study utilized network analysis techniques to evaluate 60,720 publications related to viral hepatitis research indexed in the WoS Core Collection between 2000 and 2023. The analysis was conducted using Python (Version 3.10.5) in the PyCharm development environment (Software Version 2022.1.3). The co-authorship networks were assessed using macro-level indicators such as network density (the ratio of actual to possible connections), clustering coefficient (degree of node clustering), number of components (distinct connected subgroups), and average path length (average distance between nodes). Micro-level indicators including degree centrality (importance based on the number of connections), closeness centrality (proximity to other nodes), and betweenness centrality (frequency of a node on the shortest paths between others) were also analyzed.Result: The findings reveal that the viral hepatitis research network is characterized by low network density, indicating sparse overall collaboration. However, the clustering coefficient is high, suggesting strong collaborations within small, cohesive groups. Over the years, researchers such as Stefan Zeuzem, Maria Buti, and Fabien Zoulim have played pivotal roles, serving as central figures in the network. A noticeable increase in network density and clustering from 2020-2023 suggests growing collaboration in recent years.Conclusion: This study underscores the importance of international collaboration in advancing viral hepatitis research. Key researchers have emerged as influential contributors, facilitating global research activities. Network analysis provides valuable insight into the structure of these collaborations and helps identify potential areas for future research and partnerships to combat viral hepatitis globally. The results of this study should be used to reduce global healthcare disparities, promote research collaboration, and address viral hepatitis as a global public health problem.

We evaluated the individual and combined insecticidal and insectistatic activities of the acetonic extract of Jatropha curcas seeds (JcAE) and the enzymatic extract of Serratia marcescens (SmEE) against the fall armyworm Spodoptera frugiperda. An ingestion bioassay was performed using 0 ppm, JcAE (100, 250, 1000, and 2500 ppm), SmEE (40, 80, 150, and 300 ppm), or JcAE+SmEE (1000+150, 1000+300, 2500+150, and 2500+300 ppm). We determined the weight, development, and mortality of S. frugiperda larvae and pupae, identified the major components of JcAE, and evaluated the chitinolytic activity of SmEE. Mortality was compared via Fisher’s exact test, whereas continuous variables were compared via the Kruskal‒Wallis test followed by Dunn’s test. The percentage difference was also evaluated using 0 ppm as a baseline. The two highest concentrations of JcAE (1000 and 2500 ppm) and SmEE (150 and 300 ppm) resulted in significant insecticidal and insectistactic activity because they increased the cumulative larval and pupal mortality between 77.33% and 86.67%, decreased the weights of the larvae (range, -63.02% to -80.23%) and pupae (range, -8.45% to -22.72%), and retarded their development for up to 10 and 4 days, respectively. Additionally, 1000+300 and 2500+300 ppm JcAE+SmEE increased the cumulative mortality by 93.33% and 90.0%, reduced larval and pupal weights, and prolonged larval development. Seven major fatty acids were identified as the main components of JcAE, and SmEE showed exo- and endochitinase activity. JcAE and SmEE, either individually or in combination, are alternatives for sustainable biological control of S. frugiperda.

This study presents a comprehensive investigation on an innovative mechanical system inspired by recent advancements in metamaterials, more specifically the work is focused on Origami-type structures for their intriguing mechanical properties. Originating from the specific fields such as aerospace for their lightweight and foldable characteristics, Origami mechanical devices exhibit unique non-linear stiffness, in particular, when suitably designed, they show Quasi-Zero Stiffness (QZS) characteristics within a specific working range. The QZS property, aligned with the High Static Low Dynamic (HSLD) stiffness concept, suggests promising applications such as a low-frequency mechanical passive vibration isolator. The study explores the vibration isolation characteristics of Origami-type suspensions, with a particular emphasis on their potential application as low-frequency passive vibration isolators. The Kresling Origami Module (KOM) has been selected for its compactness and compatibility with 3D printers. A detailed analysis using 3D CAD, Finite Element Analysis, and experimental testing have been carried out. The investigation includes the analysis of the influence of geometric parameters on the non-linear Force-Displacement curve. Multibody simulations validate the low-frequency isolation properties within the QZS region, and disparities in dynamic properties beyond the QZS range. The study underscores the transformative potential of Origami-type metamaterials in enhancing low-frequency vibration isolation technology. It also highlights challenges related to material properties and loading mass variations, providing valuable insights for future developments in this promising field.

A number of standard molecules are used for the molecular and histological characterization of lymphatic endothelial cells (LECs) such as Lyve1, Podoplanin (D2-40), VEGFR3, Prox1 and CD31. The number of molecules whose mutations cause lymphatic malformations or primary congenital lymphedema is considerable, but the majority of these diseases have not yet been characterized at the molecular level. Therefore, there is still considerable scope for molecular and functional studies of the lymphatic vasculature. Using RNASeq, we have previously characterized LECs under normoxic and hypoxic conditions. We used this information to compare it with immunohistochemical data. We carried out some of the immunohistology ourselves, and systematically studied the Human Protein Atlas (https://www.proteinatlas.org/). Here we describe molecules that are expressed at RNA and protein levels in LECs, hoping to stimulate future functional studies of these molecules.

Cereal crops are affected by one of the most devastating diseases worldwide, known as Fusarium head blight (FHB), with Fusarium graminearum being the most isolated causal pathogen. Another species associated to this disease is Fusarium poae. This species has been considered a relatively weak pathogen compared to F. graminearum, but its importance has increased for its occurrence in cereal grains worldwide. Considering the advantages of using B. distachyon as a plant model and the importance of F. poae in crops, our study aimed to evaluate the potential use of Brachypodium as a plant model to evaluate the compatible interaction with F. poae. For this, 12 Brachypodium spp. accessions from different countries were inoculated with a selected F. poae set of isolates. Disease severity, conidial quantification, fungal DNA biomass, and nivalenol quantification were assessed. The results showed a compatible interaction between Brachypodium accessions and Fusarium poae, which allowed the use of the model plant for future plant-pathogen interaction studies.

Abstract Background Costello syndrome (CS) is a rare RASopathy caused by a mutation in the proto-oncogene gene HRAS located on chromosome 11p15.5. It is characterized by failure to thrive, intellectual disability, coarse facies, and various cardiovascular, musculoskeletal, and dermatological abnormalities. Aims This literature review describes the various dermatological manifestations of CS in hopes of guiding dermatologists to recognize this rare neurocutaneous syndrome promptly and achieving better patient outcomes. Methods PubMed, MEDLINE, and Scopus were utilized to search for articles and studies on the dermatological features and disorders present in CS. Articles were selected and screened primarily by two reviewers. In the initial article selection process, a third reviewer was included in times of contradiction between the two primary reviewers. Twenty-three articles were ultimately included for analysis. Results CS presents myriad dermatological manifestations, the most characteristic being fine, curly to wavy hair, thick, bushy eyebrows, thin but fast-growing nails, cutaneous papillomas, and loose and redundant skin. Significant cutaneous pathologies associated with CS include eczema, acanthosis nigricans, nevi, coarse facies, and hyperpigmentation. Individuals with CS are also at increased risk for heat intolerance, hyperhidrosis, and photosensitivity. Conclusions Dermatologists must be aware of CS and its dermatological manifestations to efficiently treat the comorbidities involved with this rare neurodevelopmental disorder. CS can present similarly to Cardio-facial-cutaneous syndrome, Noonan syndrome, William syndrome, and Beckwith-Wiedemann syndrome; thus, a keen understanding of the cutaneous differences amongst the syndromes may aid in accurate diagnosis.

Background: Echovirus 11 (EV-11) is a notable enterovirus linked to severe infections in neonates, often resulting in significant morbidity and mortality. In April 2019, an EV-11 outbreak occurred in the sick neonate room at a teaching hospital in Taichung, Taiwan, posing a serious threat to the vulnerable infants in this setting. Objectives: This study aimed to identify the source and transmission dynamics of the EV-11 outbreak in the neonatal unit and evaluate the effectiveness of the infection control measures implemented to contain the outbreak. Methods: Patient records, epidemiological data, and laboratory findings were reviewed. For all cases, confirmatory tests were performed using virus isolated from cerebrospinal fluid. Rapid quarantine measures were employed. The hospital’s nosocomial infection surveillance system was used to track confirmed cases and exposed individuals to facilitate effective management and monitoring; Results: Five neonates were confirmed to have EV-11 during the outbreak. Surveillance of 10 contacts and 11 newly admitted cases yielded negative results. All infected infants eventually recovered. The cohort comprised three males and two females, with a birth weight range of 2472–4224 grams. Their gestational ages range of 34 3/7 weeks to 39 weeks; including two premature and three full-term infants. Symptoms began between 6 and 27 days of life, with fever being the consistent clinical manifestation. Strict infection control measures, including isolation of infected cases, suspension of rooming-in, and rigorous disinfection protocols, successfully halted viral spread. The outbreak was contained within 39 days, with no additional cases reported post-intervention; Conclusions: The prompt and effective response, including robust infection control protocols, was crucial for managing and containing the EV-11 outbreak in the neonatal unit. These cases underscore the significance of immediate action, stringent infection control measures, and ongoing monitoring for addressing enterovirus outbreaks in neonatal care settings.

The prediction of stock prices is a challenging task, particularly for retail investors who may lack the resources and expertise to perform sophisticated quantitative trading. This study focuses on enhancing stock price prediction for retail investors by employing advanced machine learning techniques on data from the stock exchange market. We utilize a comprehensive methodology that includes data preprocessing to handle missing values and outliers, feature engineering, cross-validation, and parameter tuning. The techniques applied include Keras Deep Neural Networks (DNN), LightGBM, LSTM, GRU, and linear regression (LR). Our proposed ensemble model, which combines time series and deep learning models, demonstrates superior performance compared to individual models. This integration of methods leads to significant improvements in prediction accuracy, providing a robust solution for retail investors.

Aging is a complex and inevitable biological process characterized by a gradual decline in physiological function, including increased oxidative stress, chronic inflammation, and mitochondrial dysfunction. While aging is a natural part of life, it is often accompanied by various disorders collectively known as age-related diseases (ARDs) or aging disorders. These include neurodegenerative diseases like Parkinson’s (PD), cardiovascular diseases, diabetes, osteoporosis, and frailty, among others. As the global population ages, the prevalence of ARDs such as PD and frailty is rising, necessitating innovative approaches to enhance healthy aging. Nutraceuticals are natural bioactive compounds in foods that offer health benefits beyond essential nutrition, which is pivotal in preventing and managing aging-related disorders. Nutraceuticals, with their antioxidant, anti-inflammatory, and neuroprotective properties, offer promising strategies to counteract these processes and promote healthy aging. This review highlights the potential of nutraceuticals as valuable adjuncts in managing PD and frailty, two conditions intricately linked to ARD. By examining the differential impacts of these bioactive compounds on the underlying mechanisms of each condition, this study underscores the promise of nutraceuticals in promoting healthy aging. The review aims to inform future research and clinical strategies by advocating for developing novel bioactive compounds, using advanced delivery technologies, and integrating personalized approaches based on genetic and epigenetic profiles. These efforts will pave the way for more precise, effective, and individualized interventions, ultimately extending health span and preventing ARD.

Migraine has a high prevalence worldwide and is one of the main disabling neurological diseases in individuals under the age of 50. In general, treatment includes the use of oral analgesics or non-steroidal anti-inflammatory drugs (NSAIDs) for mild attacks, and, for moderate or severe attacks, triptans or 5-hydroxytryptamine/serotonin (5-HT)1B/1D receptor agonists. However, the administration of antimigraine drugs in conventional oral pharmaceutical dosage forms is a challenge, since many molecules have difficulty crossing the blood-brain barrier (BBB) to reach the brain, which leads to bioavailability problems. Efforts have been made to find alternative delivery systems and/or routes for antimigraine drugs. In vivo studies have shown that it is possible to administer drugs directly into the brain via the intranasal (IN) or the nose-to-brain route, thus avoiding the need for the molecules to cross the BBB. In this field, the use of lipid nanoparticles, in particular solid lipid nanoparticles (SLN) and nanostructured lipid carriers (NLC), has shown promising results, since they have several advantages for drugs administered via the IN route, including increased absorption and reduced enzymatic degradation, improving bioavailability. Furthermore, SLN and NLC are capable of co-encapsulating drugs, promoting their simultaneous delivery to the site of therapeutic action, which can be a promising approach for the acute migraine treatment. This review highlights the potential of using SLN and NLC to improve the treatment of acute migraine via the nose-to-brain route. First sections describe the pathophysiology and the currently available pharmacological treatment for acute migraine, followed by an outline of the mechanisms underlying the nose-to-brain route. Afterwards, the main features of SLN and NLC and the most recent in vivo studies investigating the use of these nanoparticles for the treatment of acute migraine are presented.

For decades, fossil fuels have been the backbone of reliable energy systems, offering unmatched energy density and flexibility. However, as the world shifts toward renewable energy, overcoming the limitations of intermittent power sources requires a bold reimagining of energy storage and integration. Power-to-X (PtX) technologies, which convert excess renewable electricity into storable energy carriers, offer a promising solution for long-term energy storage and sector coupling. Recent advancements in machine learning (ML) have revolutionized PtX systems by enhancing efficiency, scalability, and sustainability. This review provides a detailed analysis of how ML techniques, such as deep reinforcement learning, data-driven optimization, and predictive diagnostics, are driving innovation in Power-to-Gas, Power-to-Liquid, and Power-to-Heat systems. While ML applications have shown great potential in optimizing operational decisions and managing uncertainties in renewable energy integration, challenges such as data quality, real-time processing, and scalability remain. Addressing these gaps presents important future research opportunities. These advancements are critical to decarbonizing hard-to-electrify sectors such as heavy industry, transportation, and aviation, aligning with global sustainability goals.

This study investigates the impact of positive childhood experiences (PCEs) on the subjective well-being (SWB) of college students, with psychological resilience as a mediator. A total of 1104 students from four universities in Yunnan Province, China, participated in the study. Using the Positive Childhood Experiences Scale, the Connor-Davidson Resilience Scale (CD-RISC), and the Subjective Well-being Scale, data were collected and analyzed through descriptive statistics and mediation analysis using SPSS and the PROCESS macro. Results indicate that PCEs significantly enhance SWB, and this relationship is partially mediated by psychological resilience. Specifically, psychological resilience accounted for 49.4% of the mediating effect between PCEs and SWB. These findings highlight the importance of fostering resilience to improve well-being in educational contexts. The study provides practical implications for educational interventions aimed at promoting mental health and well-being among college students.

Accurate prediction of investment returns is crucial for financial decision-making. Traditional models often struggle with the complexity and variability inherent in financial data. This article presents an advanced predictive model integrating three deep neural networks, pretraining, and ensemble learning to enhance prediction accuracy. By leveraging a combination of DNNs for handling different complexities of data, and a CNN for efficient feature extraction, our approach significantly outperforms traditional methods. Our model incorporates sophisticated data handling techniques, including extensive preprocessing and data augmentation, and benefits from pretraining and ensemble methods to improve robustness and performance. This comprehensive framework offers a robust solution for investment return prediction, addressing the shortcomings of existing models and providing enhanced accuracy and reliability.

Bioactive like resveratrol, epigallocatechin gallate, curcumin, and other polyphenols often target various signalling pathways, including NFκB, TGFβ, and Wnt/β-catenin in a degenerative joint disease like osteoarthritis (OA), by executing epigenetic-modifying activities. Epigenetic modu-lation can target genes of disease pathophysiology by histone modifications, promoter DNA methylation, and non-coding RNA expression, some of which are directly involved but less ex-plored in OA. OA patients often explore options that can improve the quality of their life in ad-dition to existing treatment with nonsteroidal anti-inflammatory drugs (NSAIDs). Although bi-oactive and natural compounds exhibited therapeutic potential against OA, several disadvantages loom, like insolubility and poor bioavailability. Nanoformulated bioactives promise a better way to alleviate OA since they also control systemic events, including metabolic, immunological, and inflammatory responses, by modulating host gut microbiota that can regulate OA pathogenesis. Recent data suggest gut dysbiosis in OA. However, limited evidence is available on the role of bioactive as epigenetic and gut modulators in ameliorating OA. Moreover, the question of whether polyphenolic bioactive modulates gut microbial response that results in epigenetic-controlling activities of OA pathogenesis is not known. This narrative review explored the interplay of nanoformulated bioactive and host gut microbiota on chondrocyte growth, metabolism, and epigenetic alteration linked with OA progression and pathogenesis.

Riemann’s functional equation is valid on the vertical line of s=1/2. Each side is a real-valued function. The Riemann’s Xi function is also a real-valued function along the vertical line of s=1/2. Through the holomorphic extensions of the Riemann zeta function, starting from the real-valued function at s=1/2 into both sides of sigma<1/2 and sigma>1/2, we can get two versions of the zeta functional equation, eq. (45). The key property of the scaling and rotational factors g(s) and g(1-s) behave as multiplicative inverses in the complex plane, eq. (48). It is deduced that the Zeta function also has multiplicative inverses, the symmetric point is at (1/2,0) in the complex plane. The moduli behave as a hyperbola. Especially, along the vertical line sigam=1/2, the amplitudes of both function g(s) and g(1-s) are equal to 1, its arguments have opposite signs. If sigma is not equal to 1/2, the amplitudes of zeta(s) and zeta(1-s) are not equal to each other, because of their multiplicative inversion relationship. It is deduced that the non-trivial zeros can only be on the vertical line of s=1/2. A gamma function vector field is given in Appendix B, and some moduli of gamma function at special points are given. Finally, another variation of the Zeta function is provided in an integral form in Appendix D. The asymptotes behave as a c₈ cyclic group for the large t values.

Low-Code Development Platforms (LCDPs) empower users to create and deploy custom software with little to no programming. These platforms streamline development, offering benefits like faster time-to-market, reduced technical barriers, and broader participation in software creation, even for those without traditional coding skills. This study explores the application of LCDPs in Precision Agriculture (PA) through a systematic literature review (SLR). By analyzing the general characteristics and challenges of LCDPs, alongside insights from existing PA research, we assess their feasibility and potential impact in agricultural contexts. Our findings suggest that LCDPs can enable farmers and agricultural professionals to create tailored applications for real-time monitoring, data analysis, and automation, enhancing farming efficiency. However, challenges such as scalability, extensibility, data security, and integration with complex IoT systems must be addressed to fully realize the benefits of LCDPs in PA. This study contributes to the growing knowledge base in agricultural technology, offering valuable insights for researchers, practitioners, and policymakers looking to leverage LCDPs for sustainable and efficient farming practices.

Copper (Cu)-based fungicides are widely used in agriculture, but excessive Cu accumulation in soils threatens crop performance, particularly in perennial fruit crops like apples. This study investigated the effects of varying Cu concentrations on the physiological responses of three apple varieties—Braeburn, Fuji, and Cripps Pink—using a callus-media system. Callus cultures were exposed to Cu concentrations ranging from 0 to 20 mg Cu kg⁻¹ to assess traits such as growth, fresh and dry weight, copper accumulation, and bioconcentration factor (BCF). Results revealed that media Cu concentration had a stronger effect than variety on most traits, especially above 10 mg Cu kg⁻¹, where copper stress reduced genetic variability. At lower Cu concentrations, varietal differences were more apparent, with Cripps Pink showing greater copper tolerance. These findings suggest that high Cu levels suppress varietal differences, leading to a uniform stress response. This study provides valuable insights into copper toxicity in apple varieties and highlights the need to maintain soil Cu levels below critical thresholds to preserve varietal performance, with potential implications for soil-plant systems.

Type I (IFN-α/β) and Type III (IFN-λ) interferon-inducible myxovirus resistance (Mx) proteins have long been recognized to inhibit a broad spectrum of RNA- and DNA-containing viruses (1-5). Mx proteins are dynamin-family GTPases of size 60-70 kDa (1-3) with different subcellular localization and antiviral activities. In the last 5 years there has been a paradigm shift in our understanding of the structures formed by human and murine Mx proteins in the cytoplasm and nucleus. We now recognize that the major human MxA protein forms structures in the cytoplasm and the major murine Mx1 forms structure in the nucleus which comprise liquid-liquid phase separated (LLPS) biomolecular condensates [membraneless organelles (MLOs)] with a gel-like internal consistency. The second human Mx protein, MxB, which had been previously observed to associate with the cytoplasmic face of nuclear pores, was also recently confirmed to represent cytoplasmic biomolecular condensates. Our overall focus now is to understand the formation, dynamics and antiviral function of biomolecular condensates of different Mx proteins in the cytoplasm and nucleus in cells of different species. We highlight the metastability and rapidly reversible hypotonicity sensing properties of MxA condensates in human cells, especially as applicable to environmental stress inflicted on the oral mucosa.

High-entropy alloys have attracted widespread attention from researchers worldwide due to their unique microstructure and outstanding mechanical properties, making them a prominent focus in the field of metallurgy. Among the various high-entropy alloys, the CoCrCuNi system was among the first alloys discovered, and it has shown significant progress in development. By employing different preparation and heat treatment processes, researchers have obtained alloys with diverse performances. The addition of various alloying elements or other components can lead to pronounced variations in the properties of CoCrCuNi high-entropy alloys. This work provides a comprehensive review of recent research progress on CoCrCuNi high-entropy alloys both domestically and internationally. It covers the preparation methods, thermodynamic and kinetic simulation calculations, as well as discussions on heat treatment processes and the influence of alloying elements on the microstructure and mechanical properties of CoCrCuNi high-entropy alloys. Finally, the review concludes with a prospective analysis and predictions for the potential applications and future directions in developing novel high-entropy alloys.

As motor capacity and rated voltage increase, the demand for motor insulation also increases. Additionally, the electric field distribution at the end of a large-scale hydro generator is extremely non-uniform, and corona discharge readily occurs. This destroys the main insulation, which significantly affects the service life of electrical machinery. Thus, the regulation of the electric field concentration at the end of a large-scale hydro generator needs to be addressed. There are many problems in resistance–capacitance chain model research. Particularly, these models cannot accurately reflect the insulation, corona structure, and electric field distribution in electrical machinery. Additionally, the corner electric field and loss distributions cannot be obtained, leading to a higher calculation error. The electric field distribution at the motor end and the loss distribution were analyzed using the finite-element method. First, by applying the basic principles of electric circuits and electromagnetic field theory, we calculated the electric field distribution at the stator bar end. Second, a 3D model of the stator bar was built using Creo Parametric, yielding a model that is more closely related to the prototype wirebar model. Then, the potential, electric field, and loss distributions around the stator bar were simulated by using COMSOL simulation software. The effects of the circular arc corner, nonlinearity coefficient, anti-corona coating length, and resistivity of the stator bar on the electric field distribution at the motor end were explored.

e Magnetic Ion Modulation (MIM) Theory presents a unified and groundbreaking mathematical framework for controlling and predicting ion behavior in both biological and non-biological systems using external magnetic fields. Central to the theory is the **Ion Modulation Constant** (Tn), a context-specific scaling factor that quantifies the number of ions modulated under specific conditions. This constant, although not universal, allows for accurate modeling of ion modulation in systems ranging from neuronal ion channels to plasma physics and quantum computing. The MIM equation incorporates ion properties such as magnetic moment and permeability, providing insights into neuromodulation for treating chronic pain and epilepsy, plasma confinement in fusion research, and stabilization of ion qubits in quantum computing. This paper includes detailed derivations of the Ion Modulation Constant, examples of its application to specific ions, and a comprehensive exploration of potential experimental validations.

Curcumin, extracted from the Curcuma longa rhizome, has gained substantial attention for its anti-inflammatory, antioxidant, and neuroprotective properties. Despite these potential benefits, its low bioavailability limits its usage and potential in clinical settings. To overcome these limitations, novel formulations have been developed using liposomal, nanoparticular and micellization technologies to improve bioavailability. The current randomized, double-blind, crossover, pharmacokinetic study compared AQUATURM^®, a novel, patented water-soluble formulation, to another commercially available and widely used curcumin supplement in 12 human subjects over a 12-hour period separated by a 7-day washout period. Plasma samples were analyzed by tandem mass spectrometry detection (UPLC-MS/MS). The area under the plasma concentration-time curve indicated that the bioavailability of AQUATURM^® was over 7-fold greater than that of the other curcumin supplement, reaching concentrations up to 20 ng/mL for up to 12-hours. This study demonstrates AQUATURM^®’s superior bioavailability to that of another, conventional, widely used curcumin supplement.

Background/Objectives: The necessity and clinical utility of routine pre-procedural blood tests (PBTs) before neuraxial blockade remain controversial. This study evaluates the effectiveness of PBTs in identifying clinically significant conditions in an outpatient setting. Methods: This single-center retrospective study involved patients who received neuraxial blockade from January 2020 to August 2023. We extracted medical information and laboratory data from the electronic medical records during the pre-procedural period. Through multivariate regression analysis, we identified patient factors associated with abnormal laboratory results. Results: Advanced age (OR, 1.021; p = 0.026) and a history of cancer (OR, 2.359; p = 0.016) were significantly associated with elevated CRP levels (>0.30 mg/dL). Severe hyperglycemia (≥200 mg/dL) was found in 24 patients (3.88%), with a history of cancer being a strong predictor (OR, 6.764; p < 0.001). No significant abnormalities were observed in PT or PTT. Reduced eGFR (

This study evaluates the effectiveness of a Random Forest model for predicting above-ground biomass in South Carolina (SC), utilizing diverse remote sensing and climatic data sources. SC, with its humid subtropical climate and varied geography, including the Atlantic coastal plain, Piedmont, and Blue Ridge Mountains, poses unique challenges for biomass estimation. We integrated global biomass datasets for 2010, MODIS vegetation indices (NDVI and EVI), Leaf Area Index (LAI) from MOD15A2H, and climate data from TerraClimate. The model was trained using 2010 data and applied to 2022 datasets to assess biomass changes. To validate the model, plot-level biomass estimates from the 2023 FIA data were interpolated using Inverse Distance Weighting (IDW). Performance evaluation showed a strong positive correlation between predicted and observed biomass, with a correlation coefficient of 0.77 and an R² value of 0.62, indicating that the model explains 62% of the variability in biomass. Comparison with IDW-interpolated biomass data resulted in a correlation coefficient of 0.64, confirming the model's validity. Although the Random Forest model demonstrated reliable predictions, the study suggests potential improvements by incorporating additional data sources and advanced modeling techniques. The findings emphasize the value of integrating remote sensing data, machine learning, and interpolation methods to enhance biomass estimation accuracy. This research provides crucial insights into biomass distribution in SC and establishes a basis for future studies on forest monitoring and carbon accounting, highlighting the importance of combining various data sources for comprehensive environmental analysis.

The burgeoning volume of genomic data, fueled by advances in sequencing technologies, demands efficient data compression solutions. Traditional algorithms like Lempel-Ziv77 (LZ77) have been foundational in offering lossless compression, yet they often fall short when applied to the highly repetitive structures typical of genomic sequences. This review delves into the evolution of LZ77 and its derivatives, exploring specialized algorithms such as prefix-free parsing, AVL grammars, and LZ-based methods tailored for genomic data. Innovations in this field have led to enhanced compression ratios and processing efficiencies by leveraging the intrinsic redundancy within genomic datasets. We critically examine a spectrum of LZ77-based algorithms, including newer adaptations for external and semi-external memory settings, and contrast their efficacy in managing large-scale genomic data. Additionally, we discuss the potential of these algorithms to facilitate the construction of data structures such as compressed suffix trees, crucial for genomic analyses. This paper aims to provide a comprehensive guide on the current landscape and future directions of data compression technologies, equipping researchers and practitioners with insights to tackle the escalating data challenges in genomics and beyond.

Microscopic image segmentation (MIS) plays a pivotal role in various fields such as medical imaging and biology. With the advent of deep learning (DL), numerous methods have emerged for automating and improving the accuracy of this crucial image analysis task. This systematic literature review (SLR) aims to provide an exhaustive overview of the state-of-the-art DL methods employed for the segmentation of microscopic images. In this review, we analyze a diverse array of studies published in the last five years, highlighting their contributions, methodologies, datasets, and performance evaluations. We explore the evolution of DL techniques and their adaptation to specific segmentation challenges, from cell and nucleus segmentation to tissue analysis. This paper, through the integration of existing knowledge, provides valuable perspectives for researchers involved in the field of microscopic image segmentation.

This paper presents a comprehensive comparative analysis of head and neck cancer (HNC) biomarkers, a prevalent but molecularly diverse malignancy. We detail the roles of key proteins and genes in tumorigenesis and progression, emphasizing their diagnostic, prognostic, and therapeutic relevance. Our bioinformatic validation reveals crucial genes such as AURKA, HMGA2, MMP1, PLAU, and SERPINE1, along with microRNAs (miRNA) linked to HNC progression. OncomiRs, including hsa-miR-21-5p, hsa-miR-31-5p, hsa-miR-221-3p, hsa-miR-222-3p, hsa-miR-196a-5p, and hsa-miR-200c-3p, drive tumorigenesis, while tumour-suppressive miRNAs like hsa-miR-375 and hsa-miR-145-5p inhibit it. Notably, hsa-miR-155-3p correlates with survival outcomes in addition to genes RAI14, S1PR5, OSBPL10, and METTL6, highlighting its prognostic potential. Future directions focus on leveraging precision medicine, novel therapeutics, and AI integration to advance personalized treatment strategies to optimize patient outcomes in HNC care.

This work addresses the analysis and characterization of deadlocks in discrete-event systems modeled by labeled Petri nets (LPNs) with undistinguishable and unobservable transitions. To provide a solution for the notorious problem, it is essential to present an effective characterization such that deadlock control and synthesis are technically and methodologically possible. To this end, we introduce the notion of dangerous implicit vectors (DIVs) which implicitly threaten the system deadlock-freedom. The set of dead markings is divided into two subsets: dead basis markings (DBMs) and dangerous implicit markings (DIMs). An algorithm is designed to compute the sets of DIVs and DIMs at a given basis state of a system. Moreover, by virtue of linear algebraic equations, we formulate sufficient conditions to identify the existence of blocking markings in an LPN. Finally, an algorithm is developed to construct an observed graph that is a compact representation of the reachability graph of a Petri net with respect to the existence of dead reaches. At the end of this paper, experiment results that illustrate the correctness and effectiveness of the reported solution are presented.

This study aims to enhance the detection and assessment of safety hazards in small-scale reservoir engineering using advanced image processing and deep learning techniques. Given the critical importance of small reservoirs in flood management, water supply, and ecological balance, the effective monitoring of their structural integrity is crucial. This paper developed a fully convolutional semantic segmentation method for hidden danger images of small reservoirs using an encoding-decoding structure, utilizing a deep learning framework of convolutional neural networks (CNNs) to process and analyze high-resolution images captured by UAVs. The method incorporated data augmentation and adaptive learning techniques to improve model accuracy under diverse environmental conditions. Finally, the quantification data of hidden dangers (length, width, area, etc.) was obtained by converting the image pixels to the actual size. Results demonstrate significant improvements in detecting structural deficiencies, such as cracks and seepage areas, with increased precision and recall rates compared to conventional methods, and the HHSN-25 network (Hidden Hazard Segmentation Network with 25 layers)proposed in this paper outperforms other methods. The main evaluation indicator mIoU of HHSN-25 is higher than other methods, reaching 87.00%, and Unet is 85.50%, Unet++is 85.55%. The proposed model achieves reliable real-time performance, allowing for early warning and effective management of potential risks. This study contributes to the development of more efficient monitoring systems for small-scale reservoirs, enhancing their safety and operational sustainability.

Differentiated thyroid cancer (DTC), comprising papillary and follicular thyroid carcinoma, is the most common thyroid malignancy and typically has a favourable prognosis when detected early. Positron emission tomography/computed tomography (PET/CT) has emerged as a valuable imaging modality, integrating metabolic and anatomical data. Although PET/CT is not usually part of the initial diagnostic process due to DTC's indolent nature and low metabolic activity, it plays an essential role in selected clinical scenarios. This includes identifying recurrence in patients with elevated thyroglobulin (Tg) levels and negative radioactive iodine (RAI) scans, evaluating metastatic disease, and guiding treatment in advanced cases. As the use of PET/CT evolves in oncology, this review explores its application in the staging, detection of recurrence, follow-up, and future challenges in managing DTC. The review also considers emerging radiotracers and the theragnostic potential of PET/CT.

The parallel connection of multiple flexible rings in raceways can significantly improve the electrical conductivity of rolling conductive devices. Multiple flexible rings are generally separated by idlers to form ring-idler rolling pairs. In the present work, alumina (Al2O3), polytetrafluoroethylene (PTFE), polyether ether ketone (PEEK), and polyimide (PI) were used as idler materials. Rolling tests for four types of ring-idler contact pairs were carried out in the air at a rotating speed of 10 rpm and a current of 60 A. It was noticed that the voltage drop was the lowest when Al2O3 was incorporated as the idler material. The energy spectrum analysis indicated material transfer between the organic idlers and the flexible ring. Especially on the flexible ring surface, the transferred organic thin film covered the gold-plated layer of the flexible ring and affected the electrical contact of the flexible ring. The transfer of Al2O3 was not obvious, and the mass fraction of Au on the flexible ring surface after the rolling test was 93.1%. The above results could help in selecting the best idler material for high-power conductive rotary joints.

Modification of canopy architecture is fundamental to productivity in grapevine. This research focused on evaluating the impact of canopy openness on photosynthetically active radiation, photosynthetic activity, and the physicochemical properties of fruit in Cabernet Sauvignon grapevines. A completely randomised design was used to compare open and closed canopies, with ten replicates per treatment (20 trees in total), during the vegetative growth period and after harvest. Key measurements included photosynthetically active radiation (PAR), daily light inte-gral (DLI), photosynthetic rate (PR), stomatal conductance (SC), intercellular CO2 concentration (IC), leaf area (LA), transpiration, ambient CO2 concentration, and temperature. Additionally, we assessed fruit quality parameters such as total soluble solids (TSS), glucose-fructose, total titratable acidity (TTA), pH, TSS/TTA, and total phenols (TP). significantly (p≤0.05) increased PAR, DLI, PR, IC, and LA, while after harvest, only PR and IC showed variation. Closed canopies increased CO2/H2O by 62.5%, while H2O/CO2 and temperature were higher in open canopies. Canopy openness increased contacts, gaps and visible sky and reduced leaf area index. Open canopy fruits showed higher TSS, glucose-fructose, pH, TSS/TTA and TP contents. Canopy openness is essential for improving light interception, photosynthetic efficiency and fruit quality in Cabernet Sauvi-gnon grapevine cultivated in northern Mexico.

Cervical cancer (CC) remains a significant global health challenge, characterized by its genetic heterogeneity and complex molecular landscape. The current study investigates the germline variants in proto-oncogenes and tumor suppressor genes among patients diagnosed with cervical cancer. Utilizing a custom NGS panel targeting 48 genes implicated in oncogenesis, we identified 148 nucleotide sequence alterations across the cohort. Of these, 35 variants (23.6%) were classified as benign, while 105 (70.9%) were categorized as variants of uncertain significance (VUS), whose clinical relevance remains to be elucidated. Moreover, we discovered seven pathogenic or likely pathogenic mutations, as well as a polymorphic variant rs1042522 in the TP53 gene, which has been previously associated with an increased risk of CC. Our findings contribute to the understanding of the molecular-genetic landscape of cervical cancer, highlighting the potential impact of rare germline mutations on the disease's pathogenesis and progression. This study underscores the importance of comprehensive genetic screening in improving the diagnostic and therapeutic strategies for cervical cancer patients.

The aquatic vegetation of the Neva Bay is characterized by high biodiversity and play an important role. In recent years, hydrotechnical works associated with transport and industrial construction have been carried out in the Gulf of Finland. As a result of industrialisation and active anthropogenic impact these areas are a place of hyperaccumulation of nutrients and pollutants. This paper presents results of the ecotoxicological state assessment of subaquatic soils and investigation of the development of macrophyte thicket ecosystems of the Neva Bay.

Machine unlearning, the process of removing the influence of specific data points from trained machine learning models, has become increasingly important in light of modern data privacy regulations, such as the GDPR and the "right to be forgotten." This paper explores the challenges and solutions associated with implementing machine unlearning in three widely used neural network architectures: Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), and Graph Neural Networks (GNNs). Each of these networks presents unique challenges due to their distinct architectures and learning processes. Techniques such as selective retraining, influence functions, and knowledge distillation have been proposed to address these challenges. The paper also introduces the concept of full parameter unlearning, which adjusts all trainable parameters using two key techniques: gradient ascent based on first-order information and Fisher information based on second-order information. These methods ensure comprehensive unlearning, but also introduce computational complexity. We discuss examples, potential solutions, and future research directions to make full parameter unlearning more scalable and efficient, thus providing a framework for balancing data privacy with model performance.

This study examines the bias in weighted least absolute deviation (WL1) estimation within the context of stationary first-order bifurcating autoregressive (BAR(1)) models, which are frequently employed to analyze binary tree-like data, including applications in cell lineage studies. Initial findings indicate that WL1 estimators can demonstrate substantial and problematic biases, especially when small to moderate sample sizes. The autoregressive parameter and the correlation between model errors influence the volume and direction of the bias. To address this issue, we propose two bootstrap-based bias-corrected estimators for the WL1 estimator. We conduct extensive simulations to assess the performance of these bias-corrected estimators. Our empirical findings demonstrate that these estimators effectively reduce the bias inherent in WL1 estimators, with their performance being particularly pronounced at the extremes of the autoregressive parameter range.

Deep brain stimulation (DBS) is currently a promising technique for psychiatric patients with severe and treatment-resistant symptoms. However, the results to date have been quite heterogeneous, and the indications for psychosurgery with DBS remain in an experimental phase. One of the major challenges limiting the advancement of DBS in psychiatric disorders is the lack of objective criteria for diagnosing certain conditions, which are often based more on clinical scales rather than measurable biological markers. Additionally, there is a limited capacity to objectively assess treatment outcomes. This overview examines the literature on the available biomarkers in psychosurgery in relation to DBS, as well as other relevant biomarkers in psychiatry with potential applicability for this treatment modality. There are five types of biomarkers: clinical/behavioral, omic, neuroimaging, electrophysiological, and neurobiochemical. The information provided by each biomarker within these categories is highly variable and may be relevant for diagnosis, response prediction, target selection, program adjustment, etc. A better understanding of biomarkers and their applications would allow DBS in psychosurgery to advance on a more objective basis, guided by the information provided by them and within the context of precision psychiatry.

The numerical algorithm used for the original multi-dimensional non-linear optimization analysis of the marine fixed pitch (screw) propulsion presented herewith, after its invention and publication has been widely used for the analysis and best fit (most probable / less uncertain) solution of over-determined non-linear mathematical problems, where the points of observations (measured data) are more than the unknown parameters in the mathematical formulation expressing the significant aspects of the observed phenomena. Any “local” attainable solution of this formidable and ingenious algorithm, may rely to the set of initial values of the unknown parameters, while not all possible solutions are expected to be physically significant for a specific problem, whereas judgment, bounds and testing are to be practiced for making sure that the attained solution is the best fit (most probable / less uncertain) one, and not necessarily the “global minimum” one. In the present paper the above algorithm is applied for the original analysis of standard ships propulsion, and particularly for the original combined application of thermo-fluid and frictional models of main engines, as well as of fixed pitch propulsion hydrodynamic models, necessary for resolving in an integrated manner the combined marine propulsion problem for standard ships under seagoing conditions.

Chylothorax is a rare complication occurring after cardio-thoracic surgical procedures. This condition presents challenges about diagnosis and treatment. Operative ductal ligation is the method of choice for relapsing or refractory cases, and it can be performed through the aid of IGC injection for the identification of chylous leakage. Our report presents the use of ICG fluorescence during VATS to successfully identify and treat a left-sided post-surgical chylothorax. The patient underwent a pulmonary wedge resection for a suspect malignant lesion and developed chylous leakage in the early postoperative period. On postoperative day 7 the patient underwent a revision thoracoscopy for hemostasis and thoracic duct ligation. ICG injections have been performed through bilateral inguinal lymph nodes and approximately 15 minutes after we performed the re-thoracoscopy with effective identification and ligation of the chyle leakage. ICG fluorescence-guided VATS is a valuable and effective method for managing postoperative chylothorax, especially for left-sided leaks. The video of the procedure can be found at: https://zenodo.org/records/13786288

Fall are significant cause of mortality and disability among older adults globally, affecting individual health and society. Addressing preventable factors, such as physical health and environmental conditions, is essential. This cross-sectional study aims to understand the factors that influence fall prevention behaviors among older adults in semi-urban community. Methods: A total of 440 older adults were randomly selected from six districts in Samutprakarn Province, Thailand. Well-trained data collectors conducted individual interviews with the participants in community-based setting. Results: Multiple logistic regression analysis was conducted, revealing that 96.3% of older adults demonstrated good fall prevention behaviors. The factors significantly associated with these behaviors included being female (OR = 4.39, 95% CI: 1.42-13.52), having good knowledge of fall prevention (OR = 9.19, 95% CI: 2.78-30.38), and perceiving the severity and susceptibility of falls, along with the benefits and barriers to prevention (OR = 11.25, 95% CI: 3.36-37.69). Additionally, sufficient access to health information (OR = 4.94, 95% CI: 1.61-15.16) and strong social support (OR = 4.06, 95% CI: 1.30-12.71) were also significant factors. In conclusion, a high percentage of older adults practiced good fall prevention behaviors, influenced by factors such as gender, knowledge, perception of fall risks, access to health information, and social support. Policy makers should prioritize enhancing health education on fall prevention, improving access to health information, and strengthening social support systems for older adults to further reduce fall-related risks and disabilities.

Previous high throughput screening studies indicated trans-eQTLs tend to be tissue specific. In this study, I probed if this feature can be used to identify tissue-specific gene regulatory networks. eQTL data (P<5e-8) for 16,259 genes were identified and their summary association statistics were obtained from the eQTLGen database. Next, eQTLs that display both cis and trans regulatory effects were selected and the association between their corresponding genes were examined by Mendelian randomization. 253 genes that exerted trans-regulatory impacts on 1,439 genes were identified. Majority of the identified gene-pairs aggregated into a gene regulatory network significantly enriched in hemo-immune processes. The robustness of finding was confirmed through simulation. Genes with higher gene expression heritabilities displayed broader regulatory impact in the network. The network displayed the scale-free topology, this provided the reason to examine the association of the network’s hub genes with the phenome. The outcome of analyses revealed GSDMB and ORMDL3 impact several disorders of immune origin and higher expression of HHEX is associated with lower triglycerides and risk of type 2 diabetes. This study reports trans-eQTLs can be used to identify tissue-specific gene regulatory networks and describes a workflow to achieve this purpose. The identified network displayed scale-free topology indicating the hub genes of a tissue-specific network could be targeted to prevent disease outcomes.

Complexity of care, adequate staffing levels, and workflow are key factors affecting nurses’ workloads. Clinical complexity classification and related staffing adjustment remain notable gaps in the current evidence limiting the capacity for optimal staffing practices. This study aimed to adapt and validate the WANNNT-SC to the Italian context to allow the assessment of newborns admitted to the NICUs. To evaluate the reliability of the tool among different profes-sionals, a correlation test was performed using Pearson’s correlation, which resulted in a strong correlation (r = 0.967, p = 0.01). In the test-retest phase, there was a significant correlation (r = 0.910 and p = 0.01). Using an analysis of variance, we found that the higher the I-WANNNT-SC score, the higher the predicted death rate (F = 13.05 and p < 0.001). The Italian Winnipeg Surgical Complex Assessment of Neonatal Nursing Needs Tool represents the first tool available for the Italian context that aims to measure the nursing workload in neonatal intensive care. It could allow adjustments in nursing staffing based on NICU activities and patient needs. This study was prospectively approved by the local Ethics Committee “Palermo 1” (Protocol CI-NICU-00).

This study examined the connection between teenage social skill development and social media use, with an emphasis on both male and female participants. Data were gathered from 101 teenagers using a snowball sampling technique in descriptive survey research. The purpose of the study was to investigate the relationship between social skill development and social media usage, as well as the frequency and intensity of social media use. The results showed that there was no statistically significant difference (ꭕ2 =1.983, p < .05, .01) in the amount of time male and female teenagers spent on social media. Furthermore, there were no statistically significant differences (t=1.29, p > α) in the impact of social media on the development of social skills between genders. Gender did not appear to be a significant impact (ꭕ2 =1.143, p > α) in the way social media affected the development of social skills, even though a higher number of females saw more significant advances in social skills, especially at higher levels of influence. The study also revealed a stronger correlation between increased social skill improvement and moderate social media use, especially in the "more than one hour" category. Excessive use, such as spending the entire day online, did not, however, considerably aid in the development of social skills. Overall, the results indicate that, when used moderately, social media can typically improve teenage social skills, with little variation in the effects observed between boys and females.

Pinosylvin and its methylated derivatives, pinosylvin monomethyl ether (PME) and dihydropinosylvin monomethyl ether (DPME), are representative phytoalexins found in Pinus species. Both PME and DPME have strong nematicidal activity against pine wood nematodes (PWNs). In this study, we investigated the resistance of Pinus densiflora to PWN after trunk injection or soil drenching with a fungal elicitor prepared from Penicillium chrysogenum. Fungal elicitor treatment by soil drenching resulted in increased accumulation of PME and DPME in the needles of the plants. When the pine trees without fungal elicitor pretreatment were inoculated with PWNs, 67.9% died after five months, but only 33.3% died among the plants pretreated with the fungal elicitor. Injection of the fungal elicitor into the trunks of P. densiflora trees also strongly increased the synthesis of PMEs. The concentration of PME in the leaves of P. densiflora reached a maximum (155.4 µg/g DW) after 4 weeks of fungal elicitor injection. The second injection of the elicitor after the first injection of the fungal elicitor also increased the accumulation of PME. To investigate PWN resistance in P. densiflora after two months of trunk injection of elicitor, the downward movement of PWNs from the top of branches to the bottom was monitored after PWNs were inoculated on the holes of the upper internodal segments of branches. The number and mobility of PWNs that moved from the bottom of branches were highly reduced in fungal elicitor-injected pine plants. This result suggests an eco-friendly approach for eliciting-induced PWN resistance in PWN-susceptible P. densiflora via fungal elicitor treatment.

From some perspective, every natural phenomenon is just a combination of many elementary random events clustering and forming a pattern. Nonetheless, the nature of random events differs from one field of study to another. This research is an attempt to determine whether volatile stock price movement, considered random, accords to the randomness of real world phenomena. To accomplish the task, a deep learning model was built, trained on preprocessed data and evaluated to calculate it's accuracy. The acquired results were then compared, and it turned out that stock prices are very similar in randomness to natural phenomena as the model trained on large amounts of stock data was increasing the accuracy of predictions of all real world parameters.

The rise of antibiotic-resistant bacteria in clinical settings has become a significant global concern. Among these bacteria, Acinetobacter baumannii stands out due to its remarkable ability to acquire resistance genes and persist in hospital environments, leading to some of the most challenging infections. Horizontal gene transfer (HGT) plays a crucial role in the evolution of this pathogen. The A. baumannii AMA205 strain, belonging to sequence type ST79, was isolated from a COVID-19 patient in Argentina in 2021. This strain’s antimicrobial resistance profile is notable as it harbors multiple resistance genes, some of which had not been previously described in this species. The AmpC family β-lactamase blaCMY-6, commonly found in Enterobacterales, had never been detected in A. baumannii before. Furthermore, this is the first ST79 strain known to carry the carbapenemase blaNDM-1 gene. Other acquired resistance genes include the carbapenemase blaOXA-23, further complicating treatment. Susceptibility testing revealed high resistance to most antibiotic families, including cefiderocol, with significant contributions from blaCMY-6 and blaNDM-1 genes to the cephalosporin and carbapenem resistance profiles. The A. baumannii AMA205 genome also contains genetic traits coding for 111 potential virulence factors, such as the iron-uptake system and biofilm-associated proteins. This study underscores A. baumannii's ability to acquire multiple resistance genes and highlights the need for alternative therapies and effective antimicrobial stewardship to control the spread of these highly resistant strains.

The construction industry, characterized by high energy use and carbon emissions, necessitates a thorough and accurate life cycle assessment (LCA). This review investigates how building information modeling (BIM) software can streamline the LCA process to improve both efficiency and precision. Although BIM has considerable potential, challenges remain, such as issues with software interoperability and a lack of standardized options for BIM-integrated LCA tools. The review also evaluates the strengths and weaknesses of various BIM software, LCA tools, and energy consumption tools, and highlights case studies of BIM-LCA integration. It provides a critical analysis of methods and techniques for BIM-LCA integration and data exchange, including the import of bills of quantities, Industry Foundation Classes (IFC), the use of BIM viewers, direct LCA calculations with BIM plugins, and calculations using LCA plugins. The study concludes with future outlooks, aiming to direct the development of improved LCA tools that offer better integration with BIM software, which is crucial for advancing sustainable construction practices.

Background/Objectives: Basal cell carcinoma (BCC), the most prevalent form of human cancer, is traditionally treated by surgical and alternative destructive or topical chemical means, each with its advantages, challenges and drawbacks. We describe our experience treating BCCs with a topical concentrate of proteolytic enzymes enriched in bromelain (CPEEB) sourced from pineapple stems. CPEEB has strong proteolytic, antitumor-proapoptotic and inflammation modulation activities, and is approved for debridement of deep burns and starting phase 3 for chronic wounds. Methods: In the first proof of concept (POC) study (published), six BCCs on 3 individuals were treated with 5-6 daily CPEEB 10% topical applications under a Zinc Oxide based occlusive dressing for 9-12 hours each during a period of up to 10 days. These patients were followed for up to 4 years. In an additional 2 POC studies, 16 patients with one BCC each were treated every other day for a total of 7 applications of topical CPEEB 5% under a variety of occlusive dressings. The wounds were followed for up to 2 months before undergoing diagnostic excisional biopsy. Results: In the first study, clinical assessment of the BCCs and 2 excisional biopsies after 6 months suggested that all lesions were eradicated with spontaneous healing within ~2 weeks without clinical or histological recurrence for over 4 years. In the 2 subsequent studies, 16 histologically diagnosed superficial and nodular BCCs were treated using 4 application techniques. Excisional histology after 2 months confirmed BCC eradication in 7 of the patients. In 9 patients, with compromised occlusive dressings, histological eradication was incomplete. Treatment was well tolerated by all patients with the expected local skin reactions that completely healed within 2-3 weeks. Conclusions: Topical CPEEB 10% and 5% resulted complete eradication of the BCC when appropriately applied. CPEEB was well tolerated in all patients and all treated sites’ erosions healed without scars in <3 weeks. Further research is necessary to corroborate the results, refine the application technique and complete the regulatory process.

Humanity is shifting its power generation to renewable energy, notably wind power. As more and more wind power plants are built, it has become important to improve the efficiency of wind power generation and reduce the manufacturing costs of wind power equipment. The mechanical multiplier gearbox is one of the most important parts of the wind power turbine, and it is now being built as a planetary gear train as these gearboxes offer a compact build capable of high power ratings in a relatively small package with coaxial input and output shafts. It is very important to select the right kinematic scheme and the other relevant parameters in the early design stages as the wrong kinematic scheme will result in a suboptimal solution. This article deals with the selection of the optimal gearbox solution for a 700 kW wind turbine application, taking into account both two-carrier and three-carrier gearbox solutions. As the optimal solution may be deducted only by systematic analysis, the proprietary 2-SPEED software has been developed for this purpose. The results of the analysis have been presented in this article, and optimal gear train solutions have been suggested for both two-carrier and three-carrier multiplier configurations.

The convergence of Class Incremental Learning (CIL) and Machine Unlearning (MU) is a rapidly developing field in machine learning, especially relevant in adaptive and privacy-sensitive environments like finance. CIL enables models to learn new data classes over time without losing previously acquired knowledge, while MU focuses on selectively forgetting specific data to comply with privacy laws or mitigate security risks. In this paper, we examine the theoretical foundations and practical applications of both approaches, particularly in the financial domain. We explore how these two paradigms interact and complement each other, discuss key algorithms, and present examples to illustrate their applications in areas such as portfolio management, fraud detection, and data privacy. Finally, we explore challenges and potential future directions in achieving optimal synergy between CIL and MU.

In this paper, we will describe the general theory of relativity. The special theory of relativity is based on the ideal inertial frames. These ideal inertial frames are assumed to exist in every event in space-time. In contrast, in the general theory of relativity, we do not have to make that assumption. Additionally, the space-time is considered to be part of the solution to the motion. Moreover, interpretation of geometrical quantities is introduced.

Generic text embeddings have demonstrated considerable success across a multitude of applications. However, these embeddings are typically derived by modeling the co-occurrence patterns within text-only corpora, which can limit their ability to generalize effectively across diverse contexts. In this study, we investigate methodologies that incorporate visual information into textual representations to overcome these limitations. Through extensive ablation studies, we introduce a novel and straightforward architecture named VisualText Fusion Network (VTFN). This architecture not only surpasses existing multimodal approaches on a range of well-established benchmark datasets but also achieves state-of-the-art performance on image-related textual datasets while utilizing significantly less training data. Our findings underscore the potential of integrating visual modalities to substantially enhance the robustness and applicability of text embeddings, paving the way for more nuanced and contextually rich semantic representations.

Aim: To evaluate the impact of applying alternative diagnostic criteria for gestational diabetes mellitus (GDM) during the COVID-19 pandemic on GDM prevalence, obstetrical and perinatal outcomes, and costs, as compared to the standard diagnostic method. Methods: A cohort of pregnant individuals undergoing GMD screening with the alternative GDM method, which uses plasma glucose (fasting or non-fasting) and HbA1c, was compared with a cohort of pregnant individuals undergoing the standard GDM screening method. Both cohorts were obtained from 6 hospitals across Catalonia, Spain, from April 2020 to April 2022. The primary outcome was large for gestational age rate at birth. Secondary outcomes were composite adverse outcomes including pregnancy complications, delivery complications, and neonatal complications. Cost differences between screening methods were also evaluated. A similar analysis was performed in the subgroup diagnosed with GDM. Results: Data were collected from 1,543 pregnant individuals in the standard screening group and 2,197 in the alternative screening group. The standard screening group had a higher GDM diagnostic rate than the alternative screening group (10.8% vs 6.9%, respectively; p<0.0001). The primary outcome (large for gestational age rate) was similar between groups: 200/1543 (13.0%) vs 303/2197 (13.8%). The adjusted OR for this outcome was 1.74 (95% CI: 0.74-4.10). An adjusted analysis showed no differences between groups in composite adverse outcomes for pregnancy complications (OR: 1.11; 95% CI: 0.91-1.36), delivery complications (OR: 0.95; 95% CI: 0.75-1.19), and neonatal complications (OR: 1.28; 95% CI: 0.94-1.75). Among individuals diagnosed with GDM, the large for gestational age rate was similar between groups: 13/166 (7.8 %) vs 15/151 (9.9 %). The OR adjusted for this outcome was 1.24 (95% CI: 0.51-3.09). An adjusted analysis showed no differences in composite adverse outcomes for pregnancy complications (OR: 1.57; 95% CI: 0.84-2.98), delivery complications (OR: 1.21; 95% CI: 0.63-2.35), and neonatal complications (OR: 1.35; 95% CI: 0.61-3.04). The mean cost (which included expenses for consumables, equipment and personnel) of the alternative screening method was 46.0 euros (22.3 SD), as compared to 85.6 euros (67.5 SD) for the standard screening method. Conclusions: In this Spanish population during the COVID-19 pandemic, GDM prevalence was lower in the alternative screening group than in the standard screening group. After adjusting for GDM risk factors, outcomes related to obstetrics, delivery, and neonatal complications were comparable between both groups. Finally, the alternative screening method was cheaper than the standard screening method.

Animals form functional units with their microbial communities, termed metaorganisms. While extensive research has been conducted on a few model animals, much remains unknown about the microbial diversity in most species. Here, we describe the taxonomic profile of the microbes from the outer gut mucus layer from the Northeast Arctic cod or also called the migrating Atlantic cod using a shotgun DNA sequencing approach. We provide metagenomes from six individuals. The results reveal three different taxonomic profile “types": Type I is dominated in numbers by Pseudomonaceae (44%) and Xanthomonadaceae (13%), Type II contains 65% Vibrionaceae and Type III contains 76% Enterobacteriaceae. This stands in sharp contrast to the bacterial diversity of the transient gut content (i.e., feces). Additionally, binning of assembled reads followed by phylogenomic analyses place a high-completeness bin of Type I within the Pseudomonas fluorescens group, one bin of Type II within the Photobacterium phosphoreum clade, and finally a bin of Type III within the Escherichia/Shigella group. In conclusion, we describe the adherent bacterial diversity in the Northeast Arctic cod's intestine using shotgun sequencing, revealing different taxonomic profiles compared to the more homogenous transient (fecal) microbiota. This suggests that the intestine contains two separate and distinct microbial populations.

Apple proliferation is among the most important diseases in European fruit production. Early and reliable detection enables farmers to respond appropriately and to prevent further spreading of the disease. Traditional phenotyping approaches by human observers consider multiple symptoms, but these are difficult to measure automatically in the field. Therefore, we investigated the potential of hyperspectral imaging in combination with data analysis by machine learning algorithms to detect the symptoms solely based on the spectral signature of collected leaf samples. In the growing seasons 2019 and 2020, we collected a total of 1,160 leaf samples. Hyperspectral imaging with a dual camera setup in spectral bands from 400 nm to 2500 nm was accompanied with subsequent PCR analysis of the samples to provide reference data for the machine learning approaches. Data processing consists of preprocessing for segmentation of the leaf area, feature extraction, classification and subsequent analysis of relevance of spectral bands. Results show, that imaging multiple leaves of a tree enhances detection results, that spectral indices are a robust means to detect the diseased trees, and that the potentials of the full spectral range can be exploited using machine learning approaches.

Bacterial structures formed from the outer membrane and the periplasm components carry biomolecules to expel cellular material and interact with other cells. These outer membrane vesicles (OMVs) can encapsulate bioactive content, which gives OMVs a high potential as alternative drug delivery vehicles or as a platform for novel vaccine development. Single-gene mutants of the hypervesiculating strain Escherichia coli JC8031 were engineered to further enhance OMV production based on metabolic network modelling and in silico gene knockout design (ΔpoxB, ΔsgbE, ΔgmhA, and ΔallD). Mutants were experimentally obtained by genome editing using CRISPR-Cas9 and tested for OMVs recovery observing an enhanced OMV production in all of them. Lipidomic analysis through LC-ESI-QTOF-MS was performed for OMVs obtained from each engineered strain and compared to the wild type E. coli JC8031 strain. The lipid profile of OMVs from the wild type E. coli JC8031 did not change significantly when compared to the mutant strains. The obtained results suggest that the vesicle production can be further improved while the obtained vesicles are not altered in their composition, allowing further study for stability and integrity for use in therapeutic settings.

This study presents an application of fractal mathematics to financial markets through the utilization of the Multifractal Model of Asset Returns (MMAR). The model is employed to analyse a 30-year dataset encompassing three distinct financial markets: the USD/NOK currency pair, the OMXS30 stock index, and the 12-month LIBOR rate. The MMAR model successfully captures essential stylized facts about financial markets, including high kurtosis, non-independent price movements, and clustered volatile days. Simulations generated by the model demonstrate realistic behaviour and multifractal characteristics. However, the model lacks a statistical method for assessing its accuracy and may underestimate kurtosis in high-kurtosis markets. Notably, the model is capable of producing simulations across a wide range of kurtosis values and can be instrumental in stress-testing portfolios. Despite its potential, the model is subject to limitations, such as the need for human judgment, ambiguous predictions, and interpretability issues. Furthermore, the model may become unreliable following a high-kurtosis event.

Ustekinumab is a fully human IgG1k monoclonal antibody that binds with high affinity and specificity to the p40 subunit of interleukins (IL-) 12 and 23, inhibiting their activity by preventing binding to their receptors. The European extension of the patent (Supplementary Protection Certificate) of ustekinumab expired on July 20, 2024. Biosimilar alternatives to ustekinumab are now an additional option for treating patients. The efficacy data for this drug in moderate-to-severe psoriasis, obtained both from clinical trials and indirect comparisons through meta-analyses, are superior to those of etanercept and adalimumab, and its safety profile is more favorable than that of tumor necrosis factor (TNF) inhibitors. Several ustekinumab biosimilars have already been approved by regulatory agencies: between October 2023 and July 2024, Wezlana® (Amgen ABP 654), Uzpruvo® (Alvotech AVT04) and Pyzchiva® (Samsung/Bioepis SB17) have been approved by both the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA). SteQeyma® (Celltrion Healthcare CT-P43) has been approved by the EMA in August 2024. Several other potential biosimilar candidates are under development, including FYB202 (Formycon), BAT2206 (Bio-Thera), DMB-3115 (Dong-A ST), QX001S (Qyuns Therapeutic), BFI-751 (BioFactura), NeuLara (Neuclone), ONS3040 (Oncobiologics), and BOW090 (Epirus Biopharmaceuticals). In most cases, these monoclonal antibodies are expressed in cell lines (e.g. Chinese Hamster Ovary, CHO) different from those used for the originator (Sp2/0 spleen cell murine myeloma); of note, the cell line of origin is not a requirement for biosimilarity in the totality-of-evidence comparison exercise and may facilitate the production and reduce the immunogenicity of biosimilars originated in CHO cultures. This narrative review will summarize the available data on characteristics of the full comparability exercises and comparative clinical trials of these drugs.

This work aims to integrate the circular economy (CE) into learning experiences to enhance learning outcome development in Higher Education (HE). Building on existing active learning research, this work aligns CE initiatives—particularly waste reduction and elimination— with the Sustainable Development Goals (SDGs). The study proposes an experiential learning framework to cultivate citizenship commitment, sustainability impact, and engineering learning outcomes through realistic problem-solving in the supply chains of small and medium enterprises (SMEs). Using the ADDIE instructional design model and Kolb’s experiential learning cycle, the approach is illustrated in a case study within an undergraduate industrial engineering module. Based on student opinion surveys and achievement metrics, results indicate that the experience significantly enhanced student engagement and motivation. This research contributes to the broader dissem-ination of CE concepts in HE and supports the advancement of SDG #4 Quality Education and #12 Sustainable Consumption and Production. Future research will explore the application of this learning experience across diverse industries and assess its impact on student learning outcomes in various educational contexts.

Sepsis is a pervasive condition that affects individuals of all ages, with significant social and economic consequences. Early diagnosis of sepsis is fundamental for establishing appropriate treatment and is based on warning scores and clinical characteristics, with positive microbiological cultures being the gold standard. Research has yet to identify a single biomarker to meet this diagnostic demand. Presepsin is a molecule that has the potential as a biomarker for diagnosing sepsis. In this paper, we present a narrative review of the diagnostic and prognostic performance of presepsin in different age groups. Given its particularities, it is identified that presepsin is a potential biomarker for sepsis at all stages of life.

Arboviruses, transmitted by mosquitoes like Aedes aegypti, pose significant public health challenges globally, particularly in tropical regions. The rapid spread and adaptation of viruses such as Dengue, Zika, and Chikungunya have emphasized the need for innovative control methods. Larvicide property of essential oils has emerged as a potential alternative to conventional insecticides. This work showed the development of an eco-friendly nanogel using a low-energy, solvent-free method, incorporating the copolymer F127 and carbopol 974p with a high concentration of essential oil from Pectis brevipedunculata (Gardner) Sch.Bip. (Pb). The resulting nanogel displayed excellent physical stability and was maintained under varying temperature conditions. Characterization techniques, including FTIR and DLS, confirmed the stable incorporation of essential oil (EOPb) within the nanogel matrix. The in vitro assays against Aedes aegypti larvae revealed that at 500 μg/mL, the mortality rates was 96.0% ± 7.0 after 24 h and 100.0% ± 0.0 after 48 h. The positive control group treated with temephos, achieved 100% mortality at both time points, validating the experimental conditions and providing a benchmark for assessing the efficacy of the nGF2002Pb nanogel. These results indicate that the nGF2002Pb demonstrates a pronounced concentration-dependent larvicidal effect against Aedes aegypti, offering an innovative and sustainable approach for arbovirus vector control.

Air pollution, particularly fine particulate matter (PM2.5), poses a significant threat to public health, especially in rapidly urbanizing areas like Lagos State. Despite the severity of the issue, there is a lack of comprehensive data on PM2.5 levels and the Air Quality Index (AQI) in Lagos. This study leverages data from AirQo, an innovative air quality monitoring network using low-cost sensors, to provide a detailed assessment of PM2.5 concentrations and AQI across eight locations in Lagos State, Nigeria. This approach offers real-time, continuous monitoring, filling a critical data gap in the region's air quality management efforts. This study uses AirQo data to assess the concentration levels and spatial distribution of PM2.5 and AQI across the selected eight locations. Data on PM2.5 concentrations were obtained from AirQo sensors deployed across the eight strategic locations in Lagos. The spatial distribution of PM2.5 levels and AQI were analyzed, and the results were compared with national and international air quality standards. The findings revealed that PM2.5 levels (minimum – 6.28 µg/m3 (Ikeja) and maximum - 204.68 µg/m3 (Banana Island) in many areas of Lagos exceed annual WHO and national air quality guidelines, indicating significant health risks. The spatial analysis identified pollution hotspots, particularly in densely populated and industrial regions. The data suggested vehicular emissions and industrial activities as major contributors to high PM2.5 levels. This study underscores the severe air quality issues in Lagos State, emphasizing the need for immediate and targeted interventions to mitigate PM2.5 pollution. The use of AirQo sensors proved effective in providing accurate and timely data for air quality assessment. It is recommended that Lagos State implements stricter emission controls, promotes cleaner technologies, and enhances public transportation systems to reduce traffic-related pollution. Additionally, expanding the air quality monitoring network and incorporating more environmental data will provide a more comprehensive understanding of air pollution impacts, thereby facilitating better policy and public health decisions.

White blood cells are crucial for immune defense in the human body, and fluctuations in their levels can lead to severe health issues. This research introduces a system designed for the localization and classification of white blood cells. The dataset utilized comprises two segments: one for localization consisting of 364 annotated images, and another for classification with 12,444 labeled images. The purpose of this study is to develop and evaluate a robust system for the accurate localization and classification of white blood cells using deep learning techniques. The study explores two localization methods—a traditional technique and a deep learning-based approach—and evaluates five deep learning architectures for classification, including three pretrained and two custom models. Upon testing these models on the dataset, the study identifies and evaluates the most effective approach. The localization process achieved an average Intersection over Union (IoU) of 71%, and the classification process reached an accuracy of 92%. The robustness of the model was further assessed by introducing different types of noise to test its resilience. The performance of this system demonstrates high accuracy and resilience to noise, which can be attributed to the use of a large dataset, our developed architectures, and our proposed methodology.

This study evaluated the post-thaw viability and in vitro fertility of ejaculated and epididymal semen from Pantaneiro bulls and characterized cell-free DNA (cfDNA) in fresh seminal plasma. Semen from five bulls was collected via electroejaculation or post-mortem epididymal extraction. Fresh semen parameters and cfDNA concentrations were assessed before cryopreservation. Post-thaw sperm kinetics were evaluated using CASA at 0 and 6 hours of incubation, and in vitro embryo development was analyzed following IVF. Data were assessed using ANOVA and logistic regression. Ejaculate samples exhibited more morphological defects than epididymal samples (15.8% vs. 1.8%, P≤0.05). Post-thaw, epididymal semen showed higher total (87.2% vs. 32.4%) and progressive (67.1% vs. 14.4%) motility at 0 h (P≤0.05), and higher motility at 6 h (38.9% vs. 11.0%, P≤0.05). In vitro fertility did not differ significantly between ejaculated (n=525 oocytes) and epididymal (n=500 oocytes) semen groups in terms of cleavage (49.6% vs. 44.2%) and blastocyst formation on D7 (26.1% vs. 22.2%, P&gt;0.05). cfDNA concentration in fresh semen ranged from 11.4 to 50.9 ng/µL. These findings indicate that epididymal sperm from Pantaneiro bulls retain high post-thaw viability and fertility. Additionally, cfDNA characterization in seminal plasma contributes to indigenous cattle preservation and advances in male fertility research.

UML (Unified Modeling Language) diagrams are graphical representations used in software engineering which play a vital role in the design and development of software systems and various engineering processes. Large, good-quality datasets containing UML diagrams are essential for different areas in the industry, research and teaching purposes, however few exist in the literature and it is common to find duplicate elements in the existing datasets. This might affect the evaluation of the models obtained when using these datasets. This paper addresses the challenge of creating a ground truth dastaset of UML diagrams, including semi-automated inspection to remove duplicates and ensure the correct labeling of all UML diagrams contained in the dataset. In particular, a dataset of six UML diagram classes has been assembled, comprising a total of 2,626 images (426 activity diagrams, 636 class diagrams, 352 component diagrams, 357 deployment diagrams, 435 sequence diagrams, and 420 use case diagrams). Importantly, unlike other existing datasets, ours contains no duplicate elements and all diagrams are correctly labeled. Our curated dataset is a valuable and unique resource for the research community because it serves as a foundation for training and evaluating various models. In this paper, we demonstrate this by training and testing several deep learning models using our dataset, achieving highly satisfactory results compared to those presented in other works in the literature. Additionally, our experimental results highlight the potential of Visual Transformers for UML diagram classification, setting our approach apart from others that predominantly used Convolutional Neural Networks for similar tasks.

The associations among increased pain sensitivity, obesity, and systemic inflammation have not been described as a direct link between BBB dysfunctions. To analyze the metabolic, behavioral, and inflammatory effects of a HFD and ultrastructural modifications in brain regions, we used an experimental model. Adult male Wistar rats were randomly assigned to one of two conditions, an ad libitum control group, or a high-fat-diet (60%) group (HFD-fed), for eight weeks. At the end of the protocol, glucose, and insulin tolerance tests were performed. Additionally, we analyzed the response to a normally innocuous mechanical stimulus and changes in motor coordination. At the end of the protocol, HFD-fed individuals presented increased HOMA-IR and metabolic syndrome (MetS) prevalence. HFD-fed rats also developed an increased nociceptive response to mechanical stimuli and neurological injury, resulting in impaired motor function. Hypothalamus and cerebellum neurons from HFD-fed presented with nuclear swelling, an absence of nucleoli, and karyolysis. These results reveal that HFD consumption affecting vital brain structures such as the cerebellum, hippocampus, and hypothalamus. This, in turn, causes neuronal damage, impairs cellular communication, and alters motricity and pain sensitivity. Understanding the link between BBB impairment and sensory and motor changes could lead to targeted therapeutic strategies.

Microbial infections in wounds can significantly delay the healing process, with bacteria often forming protective biofilms that shield them from external threats. In this study, we evaluated the impact of copper oxide-impregnated wound dressings (referred to as COD) on a bacterial mixture comprising common gram-positive and gram-negative wound pathogens encased in biofilm. The bacterial mix was exposed to COD or control dressings for 0, 1, 2, and 3 hours, and the effects were analyzed using scanning electron microscopy. After just 1 hour of exposure to COD, notable leakage of bacterial cytoplasmic contents was observed. By the 3-hour mark, the gram-negative bacteria exhibited formation of holes in their cell walls, while gram-positive bacteria showed a reduction in cell width. These findings demonstrate the ability of COD to effectively kill bacteria, even when protected by biofilm, supporting clinical observations of its efficacy in managing infected wounds.

Rechargeable solid-state batteries (SSBs) offer enhanced safety, higher energy density, and longer cycle life compared with conventional lithium-ion batteries, making them a promising solution for electric vehicles, grid storage, and other high-demand applications. However, challenges such as interface stability, low ionic conductivity, and manufacturing scalability hinder their commercial viability. This research provides a comprehensive cross-sectional thematic and bibliometric analysis of recent SSB research, focusing on four key areas: electrolyte engineering, electrode engineering, battery architecture, and performance evaluation. By analyzing publications from major databases, this study highlights the latest innovations, including improved solid electrolytes, advanced electrode materials, and novel architectures that enhance both performance and manufacturability. The findings offer valuable insights for researchers, engineers, and industry stakeholders by identifying critical gaps in the field and highlighting future research directions. The work examines progress in interface optimization, ion transport improvements, and scalable production techniques, which are crucial for advancing SSB technology. By presenting a detailed review of key developments and challenges, this study contributes to a deeper understanding of SSB progress and its potential to revolutionize the energy storage industry. The results will guide researchers in targeting high-impact areas and help industry leaders and policymakers strategize the commercial deployment of SSBs in electric vehicles, stationary energy storage, and other applications requiring reliable and efficient energy solutions.

Botulinum toxin (BoNT) is recognized as one of the most potent neurotoxins, with a lethal dose as low as 2 ng/kg, but it has also demonstrated significant therapeutic potential in controlled doses. Produced by the gram-positive anaerobic bacterium Clostridium botulinum, BoNT exists in eight distinct serotypes (A, B, C1, C2, D, E, F, and G) that disrupt neuronal transmission by targeting the soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNARE) complex at the neuromuscular junction, inhibiting the release of acetylcholine and inducing flaccid paralysis. While BoNT is notorious for its toxicity, it has been repurposed for medical use across various clinical indications. Following their approval by the Food and Drug Administration (FDA), these toxins have been associated with significant improvements in patient outcomes, particularly for individuals with specific neuromuscular disorders such as spasticity, dystonia, and cerebral palsy, as well as non-neuromuscular conditions like hyperhidrosis and cosmetic concerns. This broad spectrum of therapeutic applications that botulinum neurotoxins offer has made them an integral part of contemporary healthcare. Considering their growing importance, a detailed evaluation of their pharmacological properties, available formulations, and clinical uses is crucial. This review provides a comprehensive analysis of BoNT's molecular structure, mechanism of action, and therapeutic applications, with a particular emphasis on the most widely used BoNT-A formulations: OnabotulinumtoxinA (Botox), AbobotulinumtoxinA (Dysport), and IncobotulinumtoxinA (Xeomin). We examine their clinical efficacy, safety profiles, and differences in pharmacological properties to guide healthcare providers in selecting the most appropriate treatment for various conditions. Additionally, the review explores the challenges of BoNT therapy, such as immunogenicity and dosing considerations, while highlighting its evolving role in neurorehabilitation and conservative treatment strategies.

(1) Background: Multi-year research into the synergistic effect of cyclophosphamide and complex composite double-stranded DNA preparation (DNAmix) has made it possible to develop the novel Karanahan technology for treating malignant tumors, which is based on the eradication of cancer stem cells; (2) Methods: Pilot clinical trials for Karanahan technology were conducted in 12 patients with advanced stage IV breast cancer; (3) Results: The findings indicate that the analyzed treatment regimen ensures a local positive therapeutic response, significantly increases patient survival time, and activates the adaptive antitumor immune response. Adjustment of the basic regimen of Karanahan technology has been developed, which consists of increasing the dose of the DNAmix complex composite double stranded DNA preparation and its lymphotropic administration to the main lymph depots simultaneously with intratumoral injection; (4) Conclusions: Karanahan technology has a promising therapeutic potential in the treatment of inoperable stage IV breast cancer.

Entamoeba histolytica causes amebiasis, a significant global health issue with millions affected annually, especially in developing countries. EhDUF2419, an important protein involved in E. histolytica's queuine salvage pathway, shares homology with DNA glycosylase. However, its in-teraction network remains unclear. To explore this, we transfected E. histolytica trophozoites with a plasmid encoding Myc-tagged EhDUF2419 and achieved successful overexpression. Through immunoprecipitation with Myc antibody followed by mass spectrometry, we identified 335 proteins interacting with Myc-tagged EhDUF2419, including over 100 ribosomal proteins, along with translation initiation and elongation factors, and aminoacyl-tRNA synthetases. Ribosome purification revealed the presence of EhDUF2419 in ribosomal protein-enriched fractions. Treatment with queuosine (Q) significantly reduced EhDUF2419 protein levels and decreased Q-modified tRNA in Myc-tagged EhDUF2419 overexpressing trophozoites. This effect which is Q-dependent was not observed in strains carrying an empty vector control or overexpressing a truncated form of EhDUF2419 lacking catalytic activity. The reduction in EhDUF2419 protein levels is regulated by proteasome-mediated degradation, as evidenced by reduced degradation in the presence of MG132, a proteasome inhibitor. Our study uncovers the novel interaction of EhDUF2419 with ribosomal proteins and its regulation by the proteasome machinery, providing new insights into its role in E. histolytica and potential therapeutic strategies.

While being a workaholic supervisor may be viewed as admirable due to their commitment to work, existing findings on its effects on subordinates are mixed. Drawing upon the principle learning perspective and activation theory, we propose a curvilinear relationship between supervisor workaholism and subordinate job performance and investigate the moderating role of subordinate social-related social media usage. Specifically, we suggest that subordinate job performance increases (decreases) as supervisor workaholism increases from low (moderate) to moderate (high) levels and that this inverted U-shaped relationship becomes flatter when subordinates have higher social-related social media usage. The results, based on a multi-source survey collected from 321 supervisor-subordinate dyads, support the predictions. Our research moves beyond linear thinking and suggests that there exists an optimal level of supervisor workaholism that maximizes subordinate job performance. We also provide insights into the ongoing debate and potential ethical concerns surrounding whether employees should use social media at work. Implications for practice and future research are discussed.

Background The use of antimicrobials to treat food animals leaves antimicrobial residues in foodstuffs. The World Health Organization (WHO) defines the acceptable daily intakes (ADIs) of these residues as the dose of these antimicrobials that is safe for an average human to consume on a daily basis. We hypothesized that the low dose of ciprofloxacin classified as safe by the WHO could select for ciprofloxacin resistant strains of Klebsiella pneumoniae in a Galleria mellonella model. Objectives We aimed to evaluate if the consumption of peri-ADI doses of ciprofloxacin could select for ciprofloxacin-resistant (Ser464Phe, GyrB, ciprofloxacin MIC of 4 µg/mL) compared to -susceptible (isogenic, ciprofloxacin MIC of 0,047 µg/mL) strains of K. pneumoniae in a Galleria mellonella model. Methods A model of K. pneumoniae infection in G. mellonella larvae was used for the experiment. The larvae were inoculated with K. pneumoniae followed by 10x ADI, 1x ADI, 1/10^th ADI, 1/100^th ADI, 1/1000^th ADI doses of ciprofloxacin. Isolation of K. pneumoniae colonies was then performed on selective agar plates with and without ciprofloxacin (1 µg/mL) plates. The proportion of colonies with ciprofloxacin resistance was then calculated for each group at 24 and 48 hours. Results A significant increase was seen in the proportion of resistance for the 1x ADI and 1/10^th ADI concentration on day 2 compared to the positive control. Conclusion We found that at 48 hours, there was an enrichment of K. pneumoniae colonies with ciprofloxacin resistance in the larvae receiving 1x ADI and 1/10^th ADI concentrations of ciprofloxacin. These results suggest that the ciprofloxacin MSC_select for K. pneumoniae in this model is 1/10^th of the acceptable daily concentration (ADI) dose of ciprofloxacin, which is equivalent to 0.239 ng/µl.